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Dell T, Mesropyan N, Layer Y, Tischler V, Weinhold L, Chang J, Jansen C, Schmidt B, Jürgens M, Isaak A, Kupczyk P, Pieper CC, Meyer C, Luetkens J, Kuetting D. Photon-counting CT-derived Quantification of Hepatic Fat Fraction: A Clinical Validation Study. Radiology 2025; 314:e241677. [PMID: 40100026 DOI: 10.1148/radiol.241677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2025]
Abstract
Background Steatosis is a critical health problem, creating a growing need for opportunistic screening. Early detection may allow for effective treatment and prevention of further liver complications. Purpose To evaluate photon-counting CT (PCCT) fat quantification on contrast-enhanced scans and validate the results against fat quantification via histopathologic assessment, controlled attenuation parameter (CAP) from transient elastography, and MRI proton density fat fraction (PDFF). Materials and Methods In this prospective, observational clinical study, PCCT-derived fat fraction quantification was assessed in participants with known or suspected liver disease. Participants underwent PCCT between February 2022 and January 2024. Participants also underwent biopsy, US with CAP measurement, or MRI with a PDFF sequence for hepatic fat fraction quantification. Liver fat fraction was measured on virtual noncontrast PCCT images using spectral processing software with a three-material decomposition algorithm for fat, liver tissue, and iodine. Steatosis was graded for each modality. Correlation between PCCT-based steatosis grades and biopsy- and CAP-based grades was assessed with the Spearman correlation coefficient. Agreement between PCCT and MRI PDFF measurements was assessed with the intraclass correlation coefficient. Receiver operating characteristic curve analysis was conducted to determine the optimal PCCT fat fraction threshold for distinguishing between participants with and those without steatosis. Results The study included 178 participants, of whom 27 (mean age, 60.7 years ± 15.2 [SD]; 18 male participants) underwent liver biopsy, 26 (mean age, 60.0 years ± 18.3; 15 male participants) underwent CAP measurement, and 125 (mean age, 61.2 years ± 13.1; 70 male participants) underwent MRI PDFF measurement. There was excellent agreement between PCCT and MRI PDFF assessment of liver fat fraction (intraclass correlation coefficient, 0.91 [95% CI: 0.87, 0.94]). In stratified analysis, the intraclass correlation coefficient was 0.84 (95% CI: 0.63, 0.93) in participants with known fibrosis and 0.92 (95% CI: 0.88, 0.94) in participants without fibrosis. There was moderate correlation of PCCT-based steatosis grade with histologic (ρ = 0.65) and CAP-based (ρ = 0.45) steatosis grade. Based on the Youden index, the PCCT fat fraction threshold that best discriminated between participants with and those without steatosis was 4.8%, with a maximum achievable sensitivity of 81% (38 of 47) and a specificity of 71% (55 of 78). Conclusion PCCT in a standard clinical setting allowed for accurate estimation of liver fat fraction compared with MRI PDFF-based reference standard measurements. © RSNA, 2025 See also the editorial by Kartalis and Grigoriadis in this issue.
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Affiliation(s)
- Tatjana Dell
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Narine Mesropyan
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Yannik Layer
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Verena Tischler
- Institute of Pathology, University Hospital Bonn, Bonn, Germany
| | - Leonie Weinhold
- Institute for Medical Biometry, Informatics, and Epidemiology, Rhenish Friedrich Wilhelm University of Bonn, Bonn, Germany
| | - Johannes Chang
- Department of Internal Medicine I, Center for Cirrhosis and Portal Hypertension Bonn, University Hospital Bonn, Bonn, Germany
| | - Christian Jansen
- Department of Internal Medicine I, Center for Cirrhosis and Portal Hypertension Bonn, University Hospital Bonn, Bonn, Germany
| | - Bernhard Schmidt
- Department of Computed Tomography, Siemens Healthcare, Forchheim, Germany
| | - Markus Jürgens
- Department of Computed Tomography, Siemens Healthcare, Forchheim, Germany
| | - Alexander Isaak
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Patrick Kupczyk
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Claus Christian Pieper
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Carsten Meyer
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Julian Luetkens
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Daniel Kuetting
- Department of Diagnostic and Interventional Radiology and Quantitative Imaging Lab Bonn, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
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Hao W, Xu Z, Lin H, Yan F. Using Dual-source Photon-counting Detector CT to Simultaneously Quantify Fat and Iron Content: A Phantom Study. Acad Radiol 2024; 31:4119-4128. [PMID: 38772799 DOI: 10.1016/j.acra.2024.04.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/21/2024] [Accepted: 04/26/2024] [Indexed: 05/23/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the feasibility of using photon-counting detector computed tomography (PCD CT) to simultaneously quantify fat and iron content MATERIALS AND METHODS: Phantoms with pure fat, pure iron and fat-iron deposition were scanned by two tube voltages (120 and 140 kV) and two image quality (IQ) settings (80 and 145). Using an iron-specific three-material decomposition algorithm, virtual noniron (VNI) and virtual iron content (VIC) images were generated at quantum iterative reconstruction (QIR) strength levels 1-4. RESULTS Significant linear correlations were observed between known fat content (FC) and VNI for pure fat phantoms (r = 0.981-0.999, p < 0.001) and between known iron content (IC) and VIC for pure iron phantoms (r = 0.897-0.975, p < 0.001). In fat-iron phantoms, the measurement for fat content of 5-30% demonstrated good linearity between FC and VNI (r = 0.919-0.990, p < 0.001), and VNI were not affected by 75, 150, and 225 µmol/g iron overload (p = 0.174-0.519). The measurement for iron demonstrated a linear range of 75-225 µmol/g between IC and VIC (r = 0.961-0.994, p < 0.001) and VIC was not confounded by the coexisting 5%, 20%, and 30% fat deposition (p = 0.943-0.999). The Bland-Altman of fat and iron measurements were not significantly different at varying tube voltages and IQ settings (all p > 0.05). No significant difference in VNI and VIC at QIR 1-4. CONCLUSION PCD CT can accurately and simultaneously quantify fat and iron, including scan parameters with lower radiation dose.
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Affiliation(s)
- Wanting Hao
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Zhihan Xu
- CT Collaboration, Siemens Healthcare Ltd., No. 278 Zhouzhu Road, Shanghai 200025, China.
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China; Faculty of Medical Imaging Technology, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine.
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Wang M, Chen H, Ma Y, Bai R, Gao S, Yang L, Guo W, Zhang C, Kang C, Lan Y, Sun Y, Zhang Y, Xiao X, Hou Y. Dual-layer spectral-detector CT for detecting liver steatosis by using proton density fat fraction as reference. Insights Imaging 2024; 15:210. [PMID: 39145877 PMCID: PMC11327236 DOI: 10.1186/s13244-024-01716-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 04/15/2024] [Indexed: 08/16/2024] Open
Abstract
OBJECTIVES To evaluate the diagnostic accuracy of liver dual-layer spectral-detector CT (SDCT) derived parameters of liver parenchyma for grading steatosis with reference to magnetic resonance imaging-based proton density fat fraction (MRI-PDFF). METHODS Altogether, 320 consecutive subjects who underwent MRI-PDFF and liver SDCT examinations were recruited and prospectively enrolled from four Chinese hospital centers. Participants were classified into normal (n = 152), mild steatosis (n = 110), and moderate/severe(mod/sev) steatosis (n = 58) groups based on MRI-PDFF. SDCT liver parameters were evaluated using conventional polychromatic CT images (CTpoly), virtual mono-energetic images at 40 keV (CT40kev), the slope of the spectral attenuation curve (λ), the effective atomic number (Zeff), and liver to spleen attenuation ratio (L/S ratio). Linearity between SDCT liver parameters and MRI-PDFF was examined using Spearman correlation. Cutoff values for SDCT liver parameters in determining steatosis grades were identified using the area under the receiver-operating characteristic curve analyses. RESULTS SDCT liver parameters demonstrated a strong correlation with PDFF, particularly Zeff (rs = -0.856; p < 0.001). Zeff achieved an area under the curve (AUC) of 0.930 for detecting the presence of steatosis with a sensitivity of 89.4%, a specificity of 82.4%, and an AUC of 0.983 for detecting mod/sev steatosis with a sensitivity of 93.1%, a specificity of 93.5%, the corresponding cutoff values were 7.12 and 6.94, respectively. Zeff also exhibited good diagnostic performance for liver steatosis grading in subgroups, independent of body mass index. CONCLUSION SDCT liver parameters, particularly Zeff, exhibit excellent diagnostic accuracy for grading steatosis. CRITICAL RELEVANCE STATEMENT Dual-layer SDCT parameter, Zeff, as a more convenient and accurate imaging biomarker may serve as an alternative indicator for MRI-based proton density fat fraction, exploring the stage and prognosis of liver steatosis, and even metabolic risk assessment. KEY POINTS Liver biopsy is the standard for grading liver steatosis, but is limited by its invasive nature. The diagnostic performance of liver steatosis using SDCT-Zeff outperforms conventional CT parameters. SDCT-Zeff accurately and noninvasively assessed the grade of liver steatosis.
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Affiliation(s)
- Min Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Hongyu Chen
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Yue Ma
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Ruobing Bai
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Sizhe Gao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Linlin Yang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Wenli Guo
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Cong Zhang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Chengjun Kang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Yu Lan
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China
| | - Yanqiu Sun
- Department of Radiology, Qinghai Provincial People's Hospital, Qinghai, P.R. China
| | - Yonggao Zhang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Xigang Xiao
- Department of Radiology, The First Affiliated Hospital of Harbin Medical University, Harbin, P.R. China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, P.R. China.
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Hu N, Yan G, Tang M, Wu Y, Song F, Xia X, Chan LWC, Lei P. CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease. Eur Radiol Exp 2023; 7:72. [PMID: 37985560 PMCID: PMC10661153 DOI: 10.1186/s41747-023-00387-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/12/2023] [Indexed: 11/22/2023] Open
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.
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Affiliation(s)
- Na Hu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Gang Yan
- Department of Nuclear Medicine, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Maowen Tang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Yuhui Wu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Fasong Song
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xing Xia
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
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Polti G, Frigerio F, Del Gaudio G, Pacini P, Dolcetti V, Renda M, Angeletti S, Di Martino M, Iannetti G, Perla FM, Poggiogalle E, Cantisani V. Quantitative ultrasound fatty liver evaluation in a pediatric population: comparison with magnetic resonance imaging of liver proton density fat fraction. Pediatr Radiol 2023; 53:2458-2465. [PMID: 37698614 PMCID: PMC10635941 DOI: 10.1007/s00247-023-05749-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 09/13/2023]
Abstract
BACKGROUND Biopsy remains the gold standard for the diagnosis of hepatic steatosis, the leading cause of pediatric chronic liver disease; however, its costs call for less invasive methods. OBJECTIVE This study examined the diagnostic accuracy and reliability of quantitative ultrasound (QUS) for the assessment of liver fat content in a pediatric population, using magnetic resonance imaging proton density fat fraction (MRI-PDFF) as the reference standard. MATERIALS AND METHODS We enrolled 36 patients. MRI-PDFF involved a 3-dimensional T2*-weighted with Dixon pulse multiple-echo sequence using iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL IQ). QUS imaging relied on the ultrasound system "RS85 A" (Samsung Medison, Seoul, South Korea) and the following software: Hepato-Renal Index with automated region of interest recommendation (EzHRI), Tissue Attenuation Imaging (TAI), and Tissue Scatter Distribution Imaging (TSI). For each QUS index, receiver operating characteristic (ROC) curve analysis against MRI-PDFF was used to identify the associated cut-off value and the area under the ROC curve (AUROC). Concordance between two radiologists was assessed by intraclass correlation coefficients (ICCs) and Bland-Altman analysis. RESULTS A total of 61.1% of the sample (n=22) displayed a MRI-PDFF ≥ 5.6%; QUS cut-off values were TAI=0.625 (AUROC 0.90, confidence interval [CI] 0.77-1.00), TSI=91.95 (AUROC 0.99, CI 0.98-1.00) and EzHRI=1.215 (AUROC 0.98, CI 0.94-1.00). Inter-rater reliability was good-to-excellent for EzHRI (ICC 0.91, 95% C.I. 0.82-0.95) and TAI (ICC 0.94, 95% C.I. 0.88-0.97) and moderate to good for TSI (ICC 0.73; 95% C.I. 0.53-0.85). CONCLUSION Our results suggest that QUS can be used to reliably assess the presence and degree of pediatric hepatic steatosis.
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Affiliation(s)
- Giorgia Polti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Francesco Frigerio
- Department of Experimental Medicine, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy
| | - Giovanni Del Gaudio
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Patrizia Pacini
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Vincenzo Dolcetti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Maurizio Renda
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Sergio Angeletti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Michele Di Martino
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Giovanni Iannetti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | | | - Eleonora Poggiogalle
- Department of Experimental Medicine, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy.
| | - Vito Cantisani
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
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Duan X, Zhang Y. Establishing quality control action limits for CT number accuracy in spectral images using an American College of Radiology phantom. Med Phys 2023; 50:6071-6078. [PMID: 37475459 DOI: 10.1002/mp.16626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/14/2023] [Accepted: 06/25/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Computed tomography (CT) number accuracy is important for quality assurance in CT imaging. However, in dual-energy CT imaging, there are no widely used action limits for CT number accuracy in spectral images, information that is urgently needed. PURPOSE To establish action limits for spectral CT images using longitudinal spectral data and an American College of Radiology (ACR) phantom. METHODS An ACR accreditation phantom was scanned routinely as part of a quality control program in our institution. We selected and analyzed 57 continuous weekly scans. The CT numbers or the density values of conventional and spectral images, including virtual monoenergetic images (40, 50, 70, 120, and 200 keV), iodine maps, calcium suppressed, and virtual non-contrast images, were measured in the four inserts (solid water, bone, polyethylene, and acrylic) of the phantom. Longitudinal data were analyzed for correlation using Pearson's correlation coefficient (r) and standard deviation (SD). The SD ratios between spectral images and conventional images were calculated and the action limits for spectral images were established based on the action limits from the ACR. RESULTS Strong to very strong correlations (r > 0.70 or r < -0.70) were found among most spectral image types except the 200 keV images using solid water, polyethylene, and acrylic inserts (r = [-0.45, 0.64]). The SD ratio was highest for the 40 keV images, ranging from 2.8 to 6.5. The action limits of the bone insert were baseline ± 5.3 mg/mL for the iodine map and ranged from baseline ± 23.0 HU to baseline ± 391.9 HU for the other image types. The action limits for solid water ranged from baseline ± 4.1 HU to baseline ± 25.3 HU. The results for the polyethylene and the acrylic insert were close to those for solid water. Baselines can be established using the average of the initial 5∼10 measurements. CONCLUSIONS Using longitudinal data, we estimated the action limits for CT number accuracy in the spectral images. This paves the way for establishing a comprehensive quality control program for spectral CT imaging.
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Affiliation(s)
- Xinhui Duan
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Yue Zhang
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
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Jang W, Song JS. Non-Invasive Imaging Methods to Evaluate Non-Alcoholic Fatty Liver Disease with Fat Quantification: A Review. Diagnostics (Basel) 2023; 13:diagnostics13111852. [PMID: 37296703 DOI: 10.3390/diagnostics13111852] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/17/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Hepatic steatosis without specific causes (e.g., viral infection, alcohol abuse, etc.) is called non-alcoholic fatty liver disease (NAFLD), which ranges from non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH), fibrosis, and NASH-related cirrhosis. Despite the usefulness of the standard grading system, liver biopsy has several limitations. In addition, patient acceptability and intra- and inter-observer reproducibility are also concerns. Due to the prevalence of NAFLD and limitations of liver biopsies, non-invasive imaging methods such as ultrasonography (US), computed tomography (CT), and magnetic resonance imaging (MRI) that can reliably diagnose hepatic steatosis have developed rapidly. US is widely available and radiation-free but cannot examine the entire liver. CT is readily available and helpful for detection and risk classification, significantly when analyzed using artificial intelligence; however, it exposes users to radiation. Although expensive and time-consuming, MRI can measure liver fat percentage with magnetic resonance imaging proton density fat fraction (MRI-PDFF). Specifically, chemical shift-encoded (CSE)-MRI is the best imaging indicator for early liver fat detection. The purpose of this review is to provide an overview of each imaging modality with an emphasis on the recent progress and current status of liver fat quantification.
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Affiliation(s)
- Weon Jang
- Department of Radiology, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju 54907, Jeonbuk, Republic of Korea
- Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju 54907, Jeonbuk, Republic of Korea
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Jeonbuk, Republic of Korea
| | - Ji Soo Song
- Department of Radiology, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju 54907, Jeonbuk, Republic of Korea
- Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju 54907, Jeonbuk, Republic of Korea
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Jeonbuk, Republic of Korea
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Fetzer DT, Rosado-Mendez IM, Wang M, Robbin ML, Ozturk A, Wear KA, Ormachea J, Stiles TA, Fowlkes JB, Hall TJ, Samir AE. Pulse-Echo Quantitative US Biomarkers for Liver Steatosis: Toward Technical Standardization. Radiology 2022; 305:265-276. [PMID: 36098640 PMCID: PMC9613608 DOI: 10.1148/radiol.212808] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 04/07/2022] [Accepted: 04/14/2022] [Indexed: 11/11/2022]
Abstract
Excessive liver fat (steatosis) is now the most common cause of chronic liver disease worldwide and is an independent risk factor for cirrhosis and associated complications. Accurate and clinically useful diagnosis, risk stratification, prognostication, and therapy monitoring require accurate and reliable biomarker measurement at acceptable cost. This article describes a joint effort by the American Institute of Ultrasound in Medicine (AIUM) and the RSNA Quantitative Imaging Biomarkers Alliance (QIBA) to develop standards for clinical and technical validation of quantitative biomarkers for liver steatosis. The AIUM Liver Fat Quantification Task Force provides clinical guidance, while the RSNA QIBA Pulse-Echo Quantitative Ultrasound Biomarker Committee develops methods to measure biomarkers and reduce biomarker variability. In this article, the authors present the clinical need for quantitative imaging biomarkers of liver steatosis, review the current state of various imaging modalities, and describe the technical state of the art for three key liver steatosis pulse-echo quantitative US biomarkers: attenuation coefficient, backscatter coefficient, and speed of sound. Lastly, a perspective on current challenges and recommendations for clinical translation for each biomarker is offered.
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Affiliation(s)
| | | | - Michael Wang
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
| | - Michelle L. Robbin
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
| | - Arinc Ozturk
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
| | - Keith A. Wear
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
| | - Juvenal Ormachea
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
| | - Timothy A. Stiles
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
| | - J. Brian Fowlkes
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
| | - Timothy J. Hall
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
| | - Anthony E. Samir
- From the Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, Tex (D.T.F.); Departments of Medical Physics (I.M.R.M.,
T.J.H.) and Radiology (I.M.R.M.), University of Wisconsin, Institutes for
Medical Research, 1111 Highland Ave, Room 1005, Madison, WI 53705; General
Electric Healthcare, Milwaukee, Wis (M.W.); Department of Radiology, University
of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Department of Radiology,
Massachusetts General Hospital, Boston, Mass (A.O.); U.S. Food and Drug
Administration, Silver Spring, Md (K.A.W.); Department of Electrical and
Computer Engineering, University of Rochester, Rochester, NY (J.O.); Department
of Natural Sciences, Kettering University, Flint, Mich (T.A.S.); Departments of
Biomedical Engineering and Radiology, University of Michigan, Ann Arbor, Mich
(J.B.F.); RSNA Quantitative Imaging Biomarkers Alliance (T.J.H.); and Center for
Ultrasound Research & Translation, Department of Radiology, Massachusetts
General Hospital, Harvard Medical School, Boston, Mass (A.E.S.)
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9
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Fernández-Pérez GC, Fraga Piñeiro C, Oñate Miranda M, Díez Blanco M, Mato Chaín J, Collazos Martínez MA. Dual-energy CT: Technical considerations and clinical applications. RADIOLOGIA 2022; 64:445-455. [PMID: 36243444 DOI: 10.1016/j.rxeng.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/20/2022] [Indexed: 06/16/2023]
Abstract
Although dual-energy CT was initially described by Hounsfield in 1973, it remains underused in clinical practice. It is therefore important to emphasize the clinical benefits and limitations of this technique. Iodine mapping makes it possible to quantify the uptake of iodine, which is very important in characterizing tumors, lung perfusion, pulmonary nodules, and the tumor response to new treatments. Dual-energy CT also makes it possible to obtain virtual single-energy images and virtual images without iodinated contrast or without calcium, as well as to separate materials such as uric acid or fat and to elaborate hepatic iron overload maps. In this article, we review some of the clinical benefits and technical limitations to improve understanding of dual-energy CT and expand its use in clinical practice.
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Affiliation(s)
- G C Fernández-Pérez
- Servicio de Radiodiagnóstico, Hospital Universitario Río Hortega, Grupo Recoletas, Valladolid, Spain.
| | - C Fraga Piñeiro
- Técnico Aplicaciones Siemens Healthineers, General Electric Company, Spain
| | - M Oñate Miranda
- Servicio de Radiodiagnóstico, Hospital Universitario Río Hortega, Valladolid, Spain
| | - M Díez Blanco
- Servicio de Radiodiagnóstico, Hospital Universitario Río Hortega, Valladolid, Spain
| | - J Mato Chaín
- Servicio de Radiodiagnóstico, Hospital Universitario Río Hortega, Valladolid, Spain
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10
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Fernández-Pérez G, Fraga Piñeiro C, Oñate Miranda M, Díez Blanco M, Mato Chaín J, Collazos Martínez M. Energía Dual en TC. Consideraciones técnicas y aplicaciones clínicas. RADIOLOGIA 2022. [DOI: 10.1016/j.rx.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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11
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Hepatobiliary Dual-Energy Computed Tomography. Radiol Clin North Am 2022; 60:731-743. [DOI: 10.1016/j.rcl.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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Molwitz I, Campbell GM, Yamamura J, Knopp T, Toedter K, Fischer R, Wang ZJ, Busch A, Ozga AK, Zhang S, Lindner T, Sevecke F, Grosser M, Adam G, Szwargulski P. Fat Quantification in Dual-Layer Detector Spectral Computed Tomography: Experimental Development and First In-Patient Validation. Invest Radiol 2022; 57:463-469. [PMID: 35148536 PMCID: PMC9172900 DOI: 10.1097/rli.0000000000000858] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/09/2021] [Accepted: 12/09/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Fat quantification by dual-energy computed tomography (DECT) provides contrast-independent objective results, for example, on hepatic steatosis or muscle quality as parameters of prognostic relevance. To date, fat quantification has only been developed and used for source-based DECT techniques as fast kVp-switching CT or dual-source CT, which require a prospective selection of the dual-energy imaging mode.It was the purpose of this study to develop a material decomposition algorithm for fat quantification in phantoms and validate it in vivo for patient liver and skeletal muscle using a dual-layer detector-based spectral CT (dlsCT), which automatically generates spectral information with every scan. MATERIALS AND METHODS For this feasibility study, phantoms were created with 0%, 5%, 10%, 25%, and 40% fat and 0, 4.9, and 7.0 mg/mL iodine, respectively. Phantom scans were performed with the IQon spectral CT (Philips, the Netherlands) at 120 kV and 140 kV and 3 T magnetic resonance (MR) (Philips, the Netherlands) chemical-shift relaxometry (MRR) and MR spectroscopy (MRS). Based on maps of the photoelectric effect and Compton scattering, 3-material decomposition was done for fat, iodine, and phantom material in the image space.After written consent, 10 patients (mean age, 55 ± 18 years; 6 men) in need of a CT staging were prospectively included. All patients received contrast-enhanced abdominal dlsCT scans at 120 kV and MR imaging scans for MRR. As reference tissue for the liver and the skeletal muscle, retrospectively available non-contrast-enhanced spectral CT data sets were used. Agreement between dlsCT and MR was evaluated for the phantoms, 3 hepatic and 2 muscular regions of interest per patient by intraclass correlation coefficients (ICCs) and Bland-Altman analyses. RESULTS The ICC was excellent in the phantoms for both 120 kV and 140 kV (dlsCT vs MRR 0.98 [95% confidence interval (CI), 0.94-0.99]; dlsCT vs MRS 0.96 [95% CI, 0.87-0.99]) and in the skeletal muscle (0.96 [95% CI, 0.89-0.98]). For log-transformed liver fat values, the ICC was moderate (0.75 [95% CI, 0.48-0.88]). Bland-Altman analysis yielded a mean difference of -0.7% (95% CI, -4.5 to 3.1) for the liver and of 0.5% (95% CI, -4.3 to 5.3) for the skeletal muscle. Interobserver and intraobserver agreement were excellent (>0.9). CONCLUSIONS Fat quantification was developed for dlsCT and agreement with MR techniques demonstrated for patient liver and muscle. Hepatic steatosis and myosteatosis can be detected in dlsCT scans from clinical routine, which retrospectively provide spectral information independent of the imaging mode.
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Affiliation(s)
- Isabel Molwitz
- From the Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf
| | | | - Jin Yamamura
- From the Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf
| | - Tobias Knopp
- Institute for Biomedical Imaging, Technical University Hamburg, Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf
| | - Klaus Toedter
- Institute of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Roland Fischer
- From the Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf
- Hematology and Oncology Department, UCSF Benioff Children’s Hospital Oakland, Oakland, CA
| | - Zhiyue Jerry Wang
- Department of Radiology, Children's Health, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Alina Busch
- Center for Oncology, 2nd Medical Clinic and Polyclinic
| | - Ann-Kathrin Ozga
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Shuo Zhang
- Clinical Science, Philips GmbH Market DACH
| | - Thomas Lindner
- From the Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf
| | - Florian Sevecke
- Institute for Biomedical Imaging, Technical University Hamburg, Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf
| | - Mirco Grosser
- Institute for Biomedical Imaging, Technical University Hamburg, Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf
| | - Gerhard Adam
- From the Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf
| | - Patryk Szwargulski
- Institute for Biomedical Imaging, Technical University Hamburg, Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf
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13
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Detection of fatty liver using virtual non-contrast dual-energy CT. Abdom Radiol (NY) 2022; 47:2046-2056. [PMID: 35306577 PMCID: PMC9107401 DOI: 10.1007/s00261-022-03482-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/28/2022] [Accepted: 03/01/2022] [Indexed: 11/01/2022]
Abstract
PURPOSE Determine whether liver attenuation measured on dual-energy CT (DECT) virtual non-contrast examinations predicts the presence of fatty liver. METHODS Single-institution retrospective review from 2016 to 2020 found patients with DECT and proton density fat fraction MRI (MRI PDFF) within 30 days. MRI PDFF was the reference standard for determining hepatic steatosis. Attenuation measurements from VNC and mixed 120 kVp-like images were compared to MRI PDFF in the right and left lobes. Performance of VNC was compared to measurement of the liver-spleen attenuation difference (LSAD). RESULTS 128 patients were included (69 men, 59 women) with mean age 51.6 years (range 14-98 years). > 90% of patients received CT and MRI in the emergency department or as inpatients. Median interval between DECT and MRI PDFF was 2 days (range 0-28 days). Prevalence of fatty liver using the reference standard (MRI PDFF > 6%) was 24%. Pearson correlation coefficient between VNC and MRI- DFF was -0.64 (right) and -0.68 (left, both p < 0.0001). For LSAD, correlation was - 0.43 in both lobes (p < 0.0001). Considering MRI PDFF > 6% as diagnostic of steatosis, area under the receiver operator characteristic curve (AUC) was 0.834 and 0.872 in the right and left hepatic lobes, with an optimal threshold of 54.8 HU (right) and 52.5 HU (left), yielding sensitivity/specificity of 57%/93.9% (right) and 67.9%/90% (left). For LSAD, AUC was 0.808 (right) and 0.767 (left) with optimal sensitivity/specificity of 93.3%/57.1% (right) and 78.6%/68% (left). CONCLUSION Attenuation measured at VNC CT was moderately correlated with liver fat content and had > 90% specificity for diagnosis of fatty liver.
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14
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Cao Q, Yan C, Han X, Wang Y, Zhao L. Quantitative Evaluation of Nonalcoholic Fatty Liver in Rat by Material Decomposition Techniques using Rapid-switching Dual Energy CT. Acad Radiol 2022; 29:e91-e97. [PMID: 34654622 DOI: 10.1016/j.acra.2021.07.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the material decomposition (MD) techniques in rapid kVp switching dual-energy CT (rsDECT) for quantifying liver fat content in rats with nonalcoholic fatty liver. MATERIALS AND METHODS Fifty male Sprague-Dawley (SD) rats were divided into study group (n=37) and control group (n = 13) and underwent rsDECT examination at different intervals. All the data analysis was performed using AW4.7 workstation. The fat contents under the traditional fat(water), fat(blood), and fat(muscle) material decomposition (MD) images and the fat volume fraction (FVF) from the liver fat maps generated using multi-material decomposition (MMD) technique were measured. The pathological grades (grade 0, 1, 2 and 3) of fatty liver were determined after euthanasia. The measurement differences among different grades and the correlation of measurements with different grades was analyzed using ANOVA and Spearman correlation, respectively. A receiver operating characteristics (ROC) curve was used to analyze the diagnostic efficacies of fat contents and FVF. RESULTS There were statistically significant differences in FVF and fat contents under fat(water), fat(blood), fat(muscle) based MD images among different grades. These values correlated well with the pathological grades (R-value: 0.90, 0.75, 0.79, 0.80, all p <0.001), with FVF having the highest correlation. The area-under-the-curve in ROC of using FVF was the highest, with the cut-off value of 0.92 for sensitivity of 89.2% and specificity of 100%. CONCLUSION The rsDECT MD techniques could quantitatively evaluate the fat content of fatty liver in rat, with the FVF from MMD having the highest correlation with pathological grades.
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15
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Xu JJ, Boesen MR, Hansen SL, Ulriksen PS, Holm S, Lönn L, Hansen KL. Assessment of Liver Fat: Dual-Energy CT versus Conventional CT with and without Contrast. Diagnostics (Basel) 2022; 12:diagnostics12030708. [PMID: 35328261 PMCID: PMC8946969 DOI: 10.3390/diagnostics12030708] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 02/26/2022] [Accepted: 03/10/2022] [Indexed: 12/04/2022] Open
Abstract
We assessed the correlation between liver fat percentage using dual-energy CT (DECT) and Hounsfield unit (HU) measurements in contrast and non-contrast CT. This study included 177 patients in two patient groups: Group A (n = 125) underwent whole body non-contrast DECT and group B (n = 52) had a multiphasic DECT including a conventional non-contrast CT. Three regions of interest were placed on each image series, one in the left liver lobe and two in the right to measure Hounsfield Units (HU) as well as liver fat percentage. Linear regression analysis was performed for each group as well as combined. Receiver operating characteristic (ROC) curve was generated to establish the optimal fat percentage threshold value in DECT for predicting a non-contrast threshold of 40 HU correlating to moderate-severe liver steatosis. We found a strong correlation between fat percentage found with DECT and HU measured in non-contrast CT in group A and B individually (R2 = 0.81 and 0.86, respectively) as well as combined (R2 = 0.85). No significant difference was found when comparing venous and arterial phase DECT fat percentage measurements in group B (p = 0.67). A threshold of 10% liver fat found with DECT had 95% sensitivity and 95% specificity for the prediction of a 40 HU threshold using non-contrast CT. In conclusion, liver fat quantification using DECT shows high correlation with HU measurements independent of scan phase.
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Affiliation(s)
- Jack Junchi Xu
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (P.S.U.); (L.L.); (K.L.H.)
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
- Correspondence:
| | - Mikkel Ranum Boesen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (M.R.B.); (S.L.H.); (S.H.)
| | - Sofie Lindskov Hansen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (M.R.B.); (S.L.H.); (S.H.)
| | - Peter Sommer Ulriksen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (P.S.U.); (L.L.); (K.L.H.)
| | - Søren Holm
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (M.R.B.); (S.L.H.); (S.H.)
| | - Lars Lönn
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (P.S.U.); (L.L.); (K.L.H.)
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Kristoffer Lindskov Hansen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (P.S.U.); (L.L.); (K.L.H.)
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
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16
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Marri UK, Madhusudhan KS. Dual-Energy Computed Tomography in Diffuse Liver Diseases. JOURNAL OF GASTROINTESTINAL AND ABDOMINAL RADIOLOGY 2022. [DOI: 10.1055/s-0042-1742432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
AbstractDual-energy computed tomography (DECT) is an advancement in the field of CT, where images are acquired at two energies. Materials are identified and quantified based on their attenuation pattern at two different energy beams using various material decomposition algorithms. With its ability to identify and quantify materials such as fat, calcium, iron, and iodine, DECT adds great value to conventional CT and has innumerable applications in body imaging. Continuous technological advances in CT scanner hardware, material decomposition algorithms, and image reconstruction software have led to considerable growth of these applications. Among all organs, the liver is the most widely investigated by DECT, and DECT has shown promising results in most liver applications. In this article, we aim to provide an overview of the role of DECT in the assessment of diffuse liver diseases, mainly the deposition of fat, fibrosis, and iron and review the most relevant literature.
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Affiliation(s)
- Uday Kumar Marri
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Kumble Seetharama Madhusudhan
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
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17
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Starekova J, Hernando D, Pickhardt PJ, Reeder SB. Quantification of Liver Fat Content with CT and MRI: State of the Art. Radiology 2021; 301:250-262. [PMID: 34546125 PMCID: PMC8574059 DOI: 10.1148/radiol.2021204288] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 04/19/2021] [Accepted: 04/26/2021] [Indexed: 12/13/2022]
Abstract
Hepatic steatosis is defined as pathologically elevated liver fat content and has many underlying causes. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an increasing prevalence among adults and children. Abnormal liver fat accumulation has serious consequences, including cirrhosis, liver failure, and hepatocellular carcinoma. In addition, hepatic steatosis is increasingly recognized as an independent risk factor for the metabolic syndrome, type 2 diabetes, and, most important, cardiovascular mortality. During the past 2 decades, noninvasive imaging-based methods for the evaluation of hepatic steatosis have been developed and disseminated. Chemical shift-encoded MRI is now established as the most accurate and precise method for liver fat quantification. CT is important for the detection and quantification of incidental steatosis and may play an increasingly prominent role in risk stratification, particularly with the emergence of CT-based screening and artificial intelligence. Quantitative imaging methods are increasingly used for diagnostic work-up and management of steatosis, including treatment monitoring. The purpose of this state-of-the-art review is to provide an overview of recent progress and current state of the art for liver fat quantification using CT and MRI, as well as important practical considerations related to clinical implementation.
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Affiliation(s)
- Jitka Starekova
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.),
Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine
(S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111
Highland Ave, Madison, WI 53705
| | - Diego Hernando
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.),
Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine
(S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111
Highland Ave, Madison, WI 53705
| | - Perry J. Pickhardt
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.),
Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine
(S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111
Highland Ave, Madison, WI 53705
| | - Scott B. Reeder
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.),
Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine
(S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111
Highland Ave, Madison, WI 53705
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18
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Chatzaraki V, Born C, Kubik-Huch RA, Froehlich JM, Thali MJ, Niemann T. Influence of Radiation Dose and Reconstruction Kernel on Fat Fraction Analysis in Dual-energy CT: A Phantom Study. In Vivo 2021; 35:3147-3155. [PMID: 34697145 DOI: 10.21873/invivo.12609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/16/2021] [Accepted: 07/19/2021] [Indexed: 11/10/2022]
Abstract
BACKGROUND/AIM The quantitative evaluation of fat tissue, mainly for the determination of liver steatosis, is possible by using dual-energy computed tomography. Different photon energy acquisitions allow for estimation of attenuation coefficients. The effect of variation in radiation doses and reconstruction kernels on fat fraction estimation was investigated. MATERIALS AND METHODS A six-probe-phantom with fat concentrations of 0%, 20%, 40%, 60%, 80%, and 100% were scanned in Sn140/100 kV with radiation doses ranging between 20 and 200 mAs before and after calibration. Images were reconstructed using iterative kernels (I26,Q30,I70). RESULTS Fat fractions measured in dual-energy computed tomography (DECT) were consistent with the 20%-stepwise varying actual concentrations. Variation in radiation dose resulted in 3.1% variation of fat fraction. Softer reconstruction kernel (I26) underestimated the fat fraction (-9.1%), while quantitative (Q30) and sharper kernel (I70) overestimated fat fraction (10,8% and 13,1, respectively). CONCLUSION The fat fraction in DECT approaches the actual fat concentration when calibrated to the reconstruction kerneö. Variation of radiation dose caused an acceptable 3% variation.
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Affiliation(s)
| | - Corinna Born
- Hospital Pharmacy, Kantonsspital Baden, Baden, Switzerland
| | | | | | - Michael J Thali
- Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland
| | - Tilo Niemann
- Department of Radiology, Kantonsspital Baden, Baden, Switzerland;
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19
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Quantitative assessment of liver steatosis using ultrasound: dual-energy CT. J Med Ultrason (2001) 2021; 48:507-514. [PMID: 34536163 DOI: 10.1007/s10396-021-01136-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 07/28/2021] [Indexed: 01/20/2023]
Abstract
Reflecting the growing interest in early diagnosis of non-alcoholic fatty liver disease in recent years, the development of noninvasive and reliable fat quantification methods is needed. Dual-energy computed tomography (DE-CT) is a quantitative diagnostic imaging method that estimates the composition of the imaging target using a material decomposition technique based on the X-ray absorption characteristics peculiar to substances from DE-CT scanning using X-rays generated with different energies (tube voltage). In this review article, we first explain the basic principles and technical aspects of DE-CT. Then, we will present the current diagnostic ability of DE-CT and the factors influencing the quantitative evaluation of liver steatosis using DE-CT as compared to multi-modal methods including ultrasound and magnetic resonance imaging-based methods. In brief, DE-CT may have comparable diagnostic performance to the modern US-based liver fat measurement methods. However, the current material decomposition technique using DE-CT does not seem to have added value to the simple quantitative assessment of liver steatosis, because DE-CT measurement does not improve the accuracy of fat quantification over conventional single-energy computed tomography (SE-CT) attenuation. The most significant influencing factor for the quantitative assessment of liver steatosis using DE-CT can be hepatic iron deposition. An iron-specific multi-material decomposition algorithm correcting for the influences of iron in the liver has been under development. The current material decomposition algorithm can still have added value in a specific situation such as the quantitative assessment of liver steatosis using contrast-enhanced DE-CT. However, there is a lack of evidence for the influence of liver fibrosis in the quantitative assessment of liver steatosis using DE-CT.
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20
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Accuracy of Dual-Energy Computed Tomography Techniques for Fat Quantification in Comparison With Magnetic Resonance Proton Density Fat Fraction and Single-Energy Computed Tomography in an Anthropomorphic Phantom Environment. J Comput Assist Tomogr 2021; 45:877-887. [PMID: 34469903 DOI: 10.1097/rct.0000000000001193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To investigate in an anthropomorphic phantom study the accuracy of dual-energy computed tomography (DECT) techniques for fat quantification in comparison with magnetic resonance (MR) proton density fat fraction (PDFF) and single-energy computed tomography (SECT), using known fat content as reference standard. METHODS Between August 2018 and November 2020, organic material-based cylinders, composed of mixtures of lean and fat tissues mimics, iodine, and iron, were constructed to simulate varying fat content levels (0%, 10%, 15%, 25%, 50%, 75%, and 100%) in a parenchymal organ and were embedded into an anthropomorphic phantom simulating 3 patient sizes (circumference, 91, 126, and 161 cm). The phantom was imaged with multiecho MR, DECT, and SECT. Magnetic resonance PDFF, DECT fat fraction, and computed tomography (CT) numbers (SECT polychromatic and DECT monochromatic data, virtual unenhanced images) were estimated. Performances of MR PDFF and CT techniques to detect differences in fat content were measured using the area under the curve (AUC). Noninferiority of each CT technique relative to MR PDFF was tested using a noninferiority margin of -0.1. RESULTS MR PDFF, DECT 140 keV monochromatic data, and fat fraction most closely correlated with known fat content (R2 = 0.98, 0.98, and 0.96, respectively). Unlike SECT and all other DECT techniques, DECT fat fraction was not affected by presence of iodine (mean difference, 0.3%; 95% confidence interval [CI], -0.9% to 1.5%). Dual-energy computed tomography fat fraction showed noninferiority to MR PDFF in detecting differences of 5% in fat content in medium-sized phantoms (ΔAUC, -0.05; 95% CI, -0.08 to -0.01), and 7% in large (ΔAUC, -0.04; 95% CI, -0.0 to 0.00) or extralarge sized phantoms (ΔAUC, -0.02; 95% CI, -0.07 to 0.00). CONCLUSIONS Dual-energy computed tomography fat fraction shows linear correlation with true fat content in the range up to 50% fat fraction. Dual-energy computed tomography fat fraction has comparable estimation error and shows noninferiority to MR PDFF in detecting small differences in fat content across different body sizes.
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21
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Pickhardt PJ, Blake GM, Graffy PM, Sandfort V, Elton DC, Perez AA, Summers RM. Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard. AJR Am J Roentgenol 2021; 217:359-367. [PMID: 32936018 DOI: 10.2214/ajr.20.24415] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.
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Affiliation(s)
- Perry J Pickhardt
- Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252
| | - Glen M Blake
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom
| | - Peter M Graffy
- Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252
| | - Veit Sandfort
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
| | - Daniel C Elton
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
| | - Alberto A Perez
- Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
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22
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Zhao R, Hernando D, Harris DT, Hinshaw LA, Li K, Ananthakrishnan L, Bashir MR, Duan X, Ghasabeh MA, Kamel IR, Lowry C, Mahesh M, Marin D, Miller J, Pickhardt PJ, Shaffer J, Yokoo T, Brittain JH, Reeder SB. Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom. Med Phys 2021; 48:4375-4386. [PMID: 34105167 DOI: 10.1002/mp.15038] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 03/15/2021] [Accepted: 05/26/2021] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver. METHODS An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis. RESULTS MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R2 of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean = -0.04%, 95% limits of agreement = [-0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = -0.21%, 95% limits of agreement = [-1.47%, 1.06%]; CT: mean = -0.18HU, 95% limits of agreement = [-1.96HU, 1.6HU]) were demonstrated. CONCLUSIONS The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms.
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Affiliation(s)
- Ruiyang Zhao
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.,Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.,Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - David T Harris
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Louis A Hinshaw
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Ke Li
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.,Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Mustafa R Bashir
- Department of Radiology, Duke University, Durham, NC, USA.,Center for Advanced Magnetic Resonance Development, Duke University, Durham, NC, USA.,Division of Gastroenterology, Department of Medicine, Duke University, Durham, NC, USA
| | - Xinhui Duan
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Ihab R Kamel
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Carolyn Lowry
- Department of Radiology, Duke University, Durham, NC, USA
| | | | - Daniele Marin
- Department of Radiology, Duke University, Durham, NC, USA
| | - Jessica Miller
- Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jean Shaffer
- Department of Radiology, Duke University, Durham, NC, USA.,Center for Advanced Magnetic Resonance Development, Duke University, Durham, NC, USA
| | - Takeshi Yokoo
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Scott B Reeder
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.,Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.,Department of Medicine, University of Wisconsin, Madison, WI, USA.,Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, USA
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23
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Zhan R, Qi R, Huang S, Lu Y, Wang X, Jiang J, Ruan X, Song A. The correlation between hepatic fat fraction evaluated by dual-energy computed tomography and high-risk coronary plaques in patients with non-alcoholic fatty liver disease. Jpn J Radiol 2021; 39:763-773. [PMID: 33818707 DOI: 10.1007/s11604-021-01113-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 03/26/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE To determine the relationship between non-alcoholic fatty liver disease (NAFLD) evaluated by a hepatic fat fraction (HFF) using dual-energy computed tomography (DECT) and high-risk coronary plaques (HRP) in NAFLD patients. METHODS We conducted a matched case-control study involving 172 NAFLD individuals recruited from August 2019 to September 2020. They underwent dual-energy coronary computed tomographic angiography and were classified as no-plaque, HRP negative and HRP positive groups. HFF values were measured using multimaterial decomposition algorithm of DECT, and the differences among three groups were compared. Multiple logistic regression analysis was performed to determine the independent correlation between HFF and HRP. Spearman rank correlation was used to assess the correlations between HFF and multiple variables. RESULTS HRP positive group (15.3%) had higher HFF values than no-plaque (6.9%) and HRP negative groups (8.9%) (P < 0.001). After adjusting for confounding variables, the results indicated that HFF was an independent risk factor for HRP (OR 1.93, P < 0.001). Additionally, HFF significantly correlated with coronary artery calcium score, hepatic CT attenuation, epicardial and pericoronary adipose tissue volume, and CT attenuation (all P < 0.001). CONCLUSIONS As a new imaging marker for the quantification of liver fat, HFF was independently associated with HRP.
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Affiliation(s)
- Rui Zhan
- Department of Radiology, The Second Affiliated Hospital of Nantong University, No 6 HaiErXiang (North) Road, Chongchuan District, Nantong city, 226001, Jiangsu Province, China
| | - Rongxing Qi
- Department of Radiology, The Second Affiliated Hospital of Nantong University, No 6 HaiErXiang (North) Road, Chongchuan District, Nantong city, 226001, Jiangsu Province, China.
| | - Sheng Huang
- Department of Radiology, The Second Affiliated Hospital of Nantong University, No 6 HaiErXiang (North) Road, Chongchuan District, Nantong city, 226001, Jiangsu Province, China.
| | - Yang Lu
- Department of Radiology, The Second Affiliated Hospital of Nantong University, No 6 HaiErXiang (North) Road, Chongchuan District, Nantong city, 226001, Jiangsu Province, China
| | - Xiaoyu Wang
- Department of Radiology, The Second Affiliated Hospital of Nantong University, No 6 HaiErXiang (North) Road, Chongchuan District, Nantong city, 226001, Jiangsu Province, China
| | - Jiashen Jiang
- Department of Radiology, The Second Affiliated Hospital of Nantong University, No 6 HaiErXiang (North) Road, Chongchuan District, Nantong city, 226001, Jiangsu Province, China
| | - Xiwu Ruan
- Department of Radiology, The Second Affiliated Hospital of Nantong University, No 6 HaiErXiang (North) Road, Chongchuan District, Nantong city, 226001, Jiangsu Province, China
| | - Anyi Song
- Department of Radiology, The Second Affiliated Hospital of Nantong University, No 6 HaiErXiang (North) Road, Chongchuan District, Nantong city, 226001, Jiangsu Province, China
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Basso L, Baldi D, Mannelli L, Cavaliere C, Salvatore M, Brancato V. Investigating Dual-Energy CT Post-Contrast Phases for Liver Iron Quantification: A Preliminary Study. Dose Response 2021; 19:15593258211011359. [PMID: 34121963 PMCID: PMC8173994 DOI: 10.1177/15593258211011359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/17/2021] [Accepted: 03/29/2021] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND AND PURPOSE Quantification of hepatic virtual iron content (VIC) by using Multidetector Dual Energy Computed Tomography (DECT) has been recently investigated since this technique could offer a good compromise between accuracy and non-invasiveness for liver iron content quantification. The aim of our study is to investigate differences in VIC at different DECT time points (namely baseline and arterial, venous and tardive phases), identifying the most reliable and also exploring the underlying temporal trend of these values. MATERIALS AND METHODS Eleven patients who underwent DECT examination and were characterized by low liver fat content were included in this retrospective study. By using the Syngo.via Frontier-DE IronVNC tool, regions of interest (ROI) were placed on the VIC images at 3 hepatic levels, both in left and right liver lobes, at each DECT time point. Friedman's test followed by Bonferroni-adjusted Wilcoxon signed-rank test for post-hoc analysis was performed to assess differences between DECT timepoints. Page's L test was performed to test the temporal trend of VIC across the 4 examined timepoints. RESULTS For both liver lobes, Friedman's test followed by Bonferroni-adjusted Wilcoxon signed-rank test revealed that VIC values differed significantly when extracted from ROIs placed at the 4 different timepoints. The Page's L test for multiple comparison revealed a significant growing trend for VIC, from baseline acquisition to the fourth and last time point post-contrast agent injection. CONCLUSIONS The extraction of hepatic VIC in healthy subjects was found to be significantly influenced by the DECT time point chosen for the extrapolation of the VIC values.
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25
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Molwitz I, Leiderer M, McDonough R, Fischer R, Ozga AK, Ozden C, Tahir E, Koehler D, Adam G, Yamamura J. Skeletal muscle fat quantification by dual-energy computed tomography in comparison with 3T MR imaging. Eur Radiol 2021; 31:7529-7539. [PMID: 33770247 PMCID: PMC8452571 DOI: 10.1007/s00330-021-07820-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 01/25/2021] [Accepted: 02/19/2021] [Indexed: 12/01/2022]
Abstract
Objectives To quantify the proportion of fat within the skeletal muscle as a measure of muscle quality using dual-energy CT (DECT) and to validate this methodology with MRI. Methods Twenty-one patients with abdominal contrast-enhanced DECT scans (100 kV/Sn 150 kV) underwent abdominal 3-T MRI. The fat fraction (DECT-FF), determined by material decomposition, and HU values on virtual non-contrast-enhanced (VNC) DECT images were measured in 126 regions of interest (≥ 6 cm2) within the posterior paraspinal muscle. For validation, the MR-based fat fraction (MR-FF) was assessed by chemical shift relaxometry. Patients were categorized into groups of high or low skeletal muscle mean radiation attenuation (SMRA) and classified as either sarcopenic or non-sarcopenic, according to the skeletal muscle index (SMI) and cut-off values from non-contrast-enhanced single-energy CT. Spearman’s and intraclass correlation, Bland-Altman analysis, and mixed linear models were employed. Results The correlation was excellent between DECT-FF and MR-FF (r = 0.91), DECT VNC HU and MR-FF (r = - 0.90), and DECT-FF and DECT VNC HU (r = − 0.98). Intraclass correlation between DECT-FF and MR-FF was good (r = 0.83 [95% CI 0.71–0.90]), with a mean difference of - 0.15% (SD 3.32 [95% CI 6.35 to − 6.66]). Categorization using the SMRA yielded an eightfold difference in DECT VNC HU values between both groups (5 HU [95% CI 23–11], 42 HU [95% CI 33–56], p = 0.05). No significant relationship between DECT-FF and SMI-based classifications was observed. Conclusions Fat quantification within the skeletal muscle using DECT is both feasible and reliable. DECT muscle analysis offers a new approach to determine muscle quality, which is important for the diagnosis and therapeutic monitoring of sarcopenia, as a comorbidity associated with poor clinical outcome. Key Points • Dual-energy CT (DECT) material decomposition and virtual non-contrast-enhanced DECT HU values assess muscle fat reliably. • Virtual non-contrast-enhanced dual-energy CT HU values allow to differentiate between high and low native skeletal muscle mean radiation attenuation in contrast-enhanced DECT scans. • Measuring muscle fat by dual-energy computed tomography is a new approach for the determination of muscle quality, an important parameter for the diagnostic confirmation of sarcopenia as a comorbidity associated with poor clinical outcome.
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Affiliation(s)
- I Molwitz
- Departement of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.
| | - M Leiderer
- Departement of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - R McDonough
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - R Fischer
- Departement of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.,UCSF Benioff Children's Hospital Oakland, Oakland, CA, USA
| | - A-K Ozga
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - C Ozden
- Departement of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - E Tahir
- Departement of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - D Koehler
- Departement of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - G Adam
- Departement of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - J Yamamura
- Departement of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
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Virarkar M, Szklaruk J, Jensen CT, Taggart MW, Bhosale P. What's New in Hepatic Steatosis. Semin Ultrasound CT MR 2021; 42:405-415. [PMID: 34130852 DOI: 10.1053/j.sult.2021.03.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hepatic steatosis can lead to liver cancer, cirrhosis, and portal hypertension. There are two main types, non-alcoholic fatty liver disease (NAFLD) and alcoholic liver disease. The detection and quantification of hepatic steatosis with lifestyle changes can slow the evolution from NAFLD to steatohepatitis. Currently, the gold standard for the quantification of fat in the liver is biopsy, has some limitations. Hepatic steatosis is frequently detected during cross sectional imaging. Ultrasound (US), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) provide noninvasive assessment of liver parenchyma and can detect fat infiltration in the liver. However, the non-invasive quantification of hepatic steatosis by imaging has been challenging. Recent MRI techniques show great promise in the detection and quantification of liver fat. The aim of this article is to review the utilization of non-invasive imaging modalities for the detection and quantification of hepatic steatosis, to evaluate their advantages and limitations.
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Affiliation(s)
- Mayur Virarkar
- Department of Neuroradiology, The University of Texas Health Science Center, Houston, TX.
| | - Janio Szklaruk
- Department of Abdominal Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Corey T Jensen
- Department of Abdominal Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Melissa W Taggart
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Priya Bhosale
- Department of Abdominal Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
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Quantification of Hepatic Fat Fraction in Patients With Nonalcoholic Fatty Liver Disease: Comparison of Multimaterial Decomposition Algorithm and Fat (Water)-Based Material Decomposition Algorithm Using Single-Source Dual-Energy Computed Tomography. J Comput Assist Tomogr 2021; 45:12-17. [PMID: 33186174 PMCID: PMC7834908 DOI: 10.1097/rct.0000000000001112] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
METHODS Hepatic fat fractions were quantified by noncontrast (HFFnon-CE) and contrast-enhanced single-source dual-energy computed tomography in arterial phase (HFFAP), portal venous phase (HFFPVP) and equilibrium phase (HFFEP) using MMD in 19 nonalcoholic fatty liver disease patients. The fat concentration was measured on fat (water)-based images. As the standard of reference, magnetic resonance iterative decomposition of water and fat with echo asymmetry and least-squares estimation-iron quantification images were reconstructed to obtain HFF (HFFIDEAL-IQ). RESULTS There was a strong correlation between HFFnon-CE, HFFAP, HFFPVP, HFFEP, fat concentration and HFFIDEAL-IQ (r = 0.943, 0.923, 0.942, 0.952, and 0.726) with HFFs having better correlation with HFFIDEAL-IQ. Hepatic fat fractions did not significantly differ across scanning phases. The HFFs of 3-phase contrast-enhanced computed tomography had a good consistency with HFFnon-CE. CONCLUSIONS Hepatic fat fraction using MMD has excellent correlation with that of magnetic resonance imaging, is independent of the computed tomography scanning phases, and may be used as a routine technique for quantitative assessment of HFF.
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28
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Dual-energy CT in diffuse liver disease: is there a role? Abdom Radiol (NY) 2020; 45:3413-3424. [PMID: 32772121 DOI: 10.1007/s00261-020-02702-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/19/2020] [Accepted: 07/30/2020] [Indexed: 12/12/2022]
Abstract
Dual-energy CT (DECT) can be defined as the use of two different energy levels to identify and quantify material composition. Since its inception, DECT has benefited from remarkable improvements in hardware and clinical applications. DECT enables accurate identification and quantification of multiple materials, including fat, iron, and iodine. As a consequence, multiple studies have investigated the potential role of DECT in the assessment of diffuse liver diseases. While this role is evolving, this article aims to review the most relevant literature on use of DECT for assessment of diffuse liver diseases. Moreover, the basic concepts on DECT techniques, types of image reconstruction, and DECT-dedicated software will be described, focusing on the areas that are most relevant for the evaluation of diffuse liver diseases. Also, we will review the evidence of added value of DECT in detection and assessment of hepatocellular carcinoma which is a known risk in patients with diffuse liver disease.
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Molwitz I, Leiderer M, Özden C, Yamamura J. Dual-Energy Computed Tomography for Fat Quantification in the Liver and Bone Marrow: A Literature Review. ROFO-FORTSCHR RONTG 2020; 192:1137-1153. [DOI: 10.1055/a-1212-6017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background With dual-energy computed tomography (DECT) it is possible to quantify certain elements and tissues by their specific attenuation, which is dependent on the X-ray spectrum. This systematic review provides an overview of the suitability of DECT for fat quantification in clinical diagnostics compared to established methods, such as histology, magnetic resonance imaging (MRI) and single-energy computed tomography (SECT).
Method Following a systematic literature search, studies which validated DECT fat quantification by other modalities were included. The methodological heterogeneity of all included studies was processed. The study results are presented and discussed according to the target organ and specifically for each modality of comparison.
Results Heterogeneity of the study methodology was high. The DECT data was generated by sequential CT scans, fast-kVp-switching DECT, or dual-source DECT. All included studies focused on the suitability of DECT for the diagnosis of hepatic steatosis and for the determination of the bone marrow fat percentage and the influence of bone marrow fat on the measurement of bone mineral density. Fat quantification in the liver and bone marrow by DECT showed valid results compared to histology, MRI chemical shift relaxometry, magnetic resonance spectroscopy, and SECT. For determination of hepatic steatosis in contrast-enhanced CT images, DECT was clearly superior to SECT. The measurement of bone marrow fat percentage via DECT enabled the bone mineral density quantification more reliably.
Conclusion DECT is an overall valid method for fat quantification in the liver and bone marrow. In contrast to SECT, it is especially advantageous to diagnose hepatic steatosis in contrast-enhanced CT examinations. In the bone marrow DECT fat quantification allows more valid quantification of bone mineral density than conventional methods. Complementary studies concerning DECT fat quantification by split-filter DECT or dual-layer spectral CT and further studies on other organ systems should be conducted.
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Citation Format
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Affiliation(s)
- Isabel Molwitz
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Miriam Leiderer
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Cansu Özden
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jin Yamamura
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Jacobsen MC, Thrower SL. Multi-energy computed tomography and material quantification: Current barriers and opportunities for advancement. Med Phys 2020; 47:3752-3771. [PMID: 32453879 PMCID: PMC8495770 DOI: 10.1002/mp.14241] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 12/21/2022] Open
Abstract
Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose. In comparison to these areas, improvements in quantitative CT have lagged behind. While this could be a consequence of the technological limitations of conventional CT, advanced dual-energy CT (DECT) and photon-counting detector CT (PCD-CT) offer new opportunities for quantitation. Routine use of DECT is becoming more widely available and PCD-CT is rapidly developing. This review covers efforts to address an unmet need for improved quantitative imaging to better characterize disease, identify biomarkers, and evaluate therapeutic response, with an emphasis on multi-energy CT applications. The review will primarily discuss applications that have utilized quantitative metrics using both conventional and DECT, such as bone mineral density measurement, evaluation of renal lesions, and diagnosis of fatty liver disease. Other topics that will be discussed include efforts to improve quantitative CT volumetry and radiomics. Finally, we will address the use of quantitative CT to enhance image-guided techniques for surgery, radiotherapy and interventions and provide unique opportunities for development of new contrast agents.
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Affiliation(s)
- Megan C. Jacobsen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sara L. Thrower
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Yoo JJ, Lim YS, Kim MS, Lee B, Kim BY, Kim Z, Lee JE, Lee MH, Kim SG, Kim YS. Risk of fatty liver after long-term use of tamoxifen in patients with breast cancer. PLoS One 2020; 15:e0236506. [PMID: 32730287 PMCID: PMC7392315 DOI: 10.1371/journal.pone.0236506] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 07/07/2020] [Indexed: 01/22/2023] Open
Abstract
Background Few studies report the effects of tamoxifen intake and the occurrence of de novo fatty liver and the deterioration of existing fatty liver. The aim of this study was to investigate the effects of tamoxifen on fatty change of liver over time and also the impact of fatty liver on the prognosis of patients with breast cancer. Methods This was a single-center, retrospective study of patients who were diagnosed with primary breast cancer from January 2007 to July 2017. 911 consecutive patients were classified into three groups according to treatment method: tamoxifen group, aromatase inhibitor (AI) group, and control group. Results Median treatment duration was 49 months (interquartile range, IQR; 32–58) and median observational period was 85 months (IQR; 50–118). Long-term use of tamoxifen significantly aggravated fatty liver status compared to AI or control groups [hazard ratio (HR): 1.598, 95% confidence interval (CI): 1.173–2.177, P = 0.003] after adjusting other factors. When analyzed separately depending on pre-existing fatty liver at baseline, tamoxifen was involved in the development of de novo fatty liver [HR: 1.519, 95% CI: 1.100–2.098, P = 0.011) and had greater effect on fatty liver worsening (HR: 2.103, 95% CI: 1.156–3.826, P = 0.015). However, the progression of fatty liver did not significantly affect the mortality of breast cancer patients. Conclusions Tamoxifen had a significant effect on the fatty liver status compared to other treatment modalities in breast cancer patients. Although fatty liver did not affect the prognosis of breast cancer, meticulous attention to cardiovascular disease or other metabolic disease should be paid when used for a long time.
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Affiliation(s)
- Jeong-Ju Yoo
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Soonchunhyang University School of Medicine Bucheon Hospital, Bucheon, Korea
| | - Yong Seok Lim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Soonchunhyang University School of Medicine Bucheon Hospital, Bucheon, Korea
| | - Min Sung Kim
- Department of Internal Medicine, Soonchunhyang University Gumi Hospital, Gumi, Korea
| | - Bora Lee
- Department of Biostatistics, Graduate School of Chung-Ang University, Seoul, Republic of Korea
| | - Bo-Yeon Kim
- Division of Endocrinology, Department of Internal Medicine, Soonchunhyang University School of Medicine Bucheon Hospital, Bucheon, Korea
| | - Zisun Kim
- Department of Surgery, Soonchunhyang University School of Medicine Bucheon Hospital, Bucheon, Korea
| | - Ji Eun Lee
- Department of Radiology, Soonchunhyang University School of Medicine Bucheon Hospital, Bucheon, Korea
| | - Min Hee Lee
- Department of Radiology, Soonchunhyang University School of Medicine Bucheon Hospital, Bucheon, Korea
| | - Sang Gyune Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Soonchunhyang University School of Medicine Bucheon Hospital, Bucheon, Korea
- * E-mail:
| | - Young Seok Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Soonchunhyang University School of Medicine Bucheon Hospital, Bucheon, Korea
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Han A, Byra M, Heba E, Andre MP, Erdman JW, Loomba R, Sirlin CB, O'Brien WD. Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks. Radiology 2020; 295:342-350. [PMID: 32096706 DOI: 10.1148/radiol.2020191160] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. Materials and Methods A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. Results A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years ± 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years ± 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. Conclusion Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lockhart and Smith in this issue.
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Affiliation(s)
- Aiguo Han
- From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.)
| | - Michal Byra
- From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.)
| | - Elhamy Heba
- From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.)
| | - Michael P Andre
- From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.)
| | - John W Erdman
- From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.)
| | - Rohit Loomba
- From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.)
| | - Claude B Sirlin
- From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.)
| | - William D O'Brien
- From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.)
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Zhang M. Imaging Biomarkers for Nonalcoholic Fatty Liver Disease. Acad Radiol 2019; 26:869-871. [PMID: 31060980 DOI: 10.1016/j.acra.2019.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 04/07/2019] [Indexed: 10/26/2022]
Affiliation(s)
- Man Zhang
- Department of Radiology, University of Michigan, 1500 E Medical Center Drive, Ann Arbor, MI 48109.
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Cao Q, Shang S, Han X, Cao D, Zhao L. Evaluation on Heterogeneity of Fatty Liver in Rats: A Multiparameter Quantitative Analysis by Dual Energy CT. Acad Radiol 2019; 26:e47-e55. [PMID: 30041922 DOI: 10.1016/j.acra.2018.05.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 05/27/2018] [Accepted: 05/27/2018] [Indexed: 01/21/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of using rapid kVp switching dual energy computed tomography (rsDECT) for the multi-parameter analysis of the heterogeneity of fatty liver in rat. MATERIALS AND METHODS Twenty-four male rats were randomly divided into experimental (n = 16) and control (n = 8) groups. Four rats in the experimental group and two in the control group were examined by rsDECT every 3 weeks starting from week 8. The liver fat contents (LFC) of the left and right liver lobes were measured on the fat(water)-based material decomposition images to calculate fat content and CT value of liver and spleen were measured on the 70keV monochromatic images to calculate the liver-to-spleen CT value ratio [(L/S)70 keV] and difference at 70keV[(L-S)70 keV], and the spectral curve slopes of the left and right liver lobes (Slope-L, Slope-R). Measurements were evaluated statistically. RESULTS The statistical analysis showed that the (L/S)70 keV, (L-S)70 keV, Slope and LFC in the different fatty liver groups were all significantly different (p < 0.05). Pearson correlation analysis results showed that the rsDECT results of the left and right liver lobes correlated with the fat percentage from pathological analysis (p < 0.05), with the left liver lobe having better correlation. Paired t-tests showed that the fat percentage of the left liver lobe was significantly higher than that of the right one, while (L/S)70 keV, and (L-S)70 keV were significantly lower. Diagnostic analysis using ROC curve showed that the areas under the curves with parameters of the left liver lobe were also greater than those of the right liver lobe. CONCLUSION rsDECT multi-parameter imaging could quantitatively evaluate the heterogeneity of fat deposition in the liver, providing valuable information for the accurate and effective assessment of the heterogeneity of fatty liver.
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Inter-platform reproducibility of ultrasonic attenuation and backscatter coefficients in assessing NAFLD. Eur Radiol 2019; 29:4699-4708. [PMID: 30783789 DOI: 10.1007/s00330-019-06035-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 12/24/2018] [Accepted: 01/22/2019] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To assess inter-platform reproducibility of ultrasonic attenuation coefficient (AC) and backscatter coefficient (BSC) estimates in adults with known/suspected nonalcoholic fatty liver disease (NAFLD). METHODS This HIPAA-compliant prospective study was approved by an institutional review board; informed consent was obtained. Participants with known/suspected NAFLD were recruited and underwent same-day liver examinations with clinical ultrasound scanner platforms from two manufacturers. Each participant was scanned by the same trained sonographer who performed multiple data acquisitions in the right liver lobe using a lateral intercostal approach. Each data acquisition recorded a B-mode image and the underlying radio frequency (RF) data. AC and BSC were calculated using the reference phantom method. Inter-platform reproducibility was evaluated for AC and log-transformed BSC (logBSC = 10log10BSC) by intraclass correlation coefficient (ICC), Pearson's correlation, Bland-Altman analysis with computation of limits of agreement (LOAs), and within-subject coefficient of variation (wCV; applicable to AC). RESULTS Sixty-four participants were enrolled. Mean AC values measured using the two platforms were 0.90 ± 0.13 and 0.94 ± 0.15 dB/cm/MHz while mean logBSC values were - 30.6 ± 5.0 and - 27.9 ± 5.6 dB, respectively. Inter-platform ICC was 0.77 for AC and 0.70 for log-transformed BSC in terms of absolute agreement. Pearson's correlation coefficient was 0.81 for AC and 0.80 for logBSC. Ninety-five percent LOAs were - 0.21 to 0.13 dB/cm/MHz for AC, and - 9.48 to 3.98 dB for logBSC. The wCV was 7% for AC. CONCLUSIONS Hepatic AC and BSC are reproducible across two different ultrasound platforms in adults with known or suspected NAFLD. KEY POINTS • Ultrasonic attenuation coefficient and backscatter coefficient are reproducible between two different ultrasound platforms in adults with NAFLD. • This inter-platform reproducibility may qualify quantitative ultrasound biomarkers for generalized clinical application in patients with suspected/known NAFLD.
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Zhang YN, Fowler KJ, Hamilton G, Cui JY, Sy EZ, Balanay M, Hooker JC, Szeverenyi N, Sirlin CB. Liver fat imaging-a clinical overview of ultrasound, CT, and MR imaging. Br J Radiol 2018; 91:20170959. [PMID: 29722568 PMCID: PMC6223150 DOI: 10.1259/bjr.20170959] [Citation(s) in RCA: 171] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Hepatic steatosis is a frequently encountered imaging finding that may indicate chronic liver disease, the most common of which is non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease is implicated in the development of systemic diseases and its progressive phenotype, non-alcoholic steatohepatitis, leads to increased liver-specific morbidity and mortality. With the rising obesity epidemic and advent of novel therapeutics aimed at altering metabolism, there is a growing need to quantify and monitor liver steatosis. Imaging methods for assessing steatosis range from simple and qualitative to complex and highly accurate metrics. Ultrasound may be appropriate in some clinical instances as a screening modality to identify the presence of abnormal liver morphology. However, it lacks sufficient specificity and sensitivity to constitute a diagnostic modality for instigating and monitoring therapy. Newer ultrasound techniques such as quantitative ultrasound show promise in turning qualitative assessment of steatosis on conventional ultrasound into quantitative measurements. Conventional unenhanced CT is capable of detecting and quantifying moderate to severe steatosis but is inaccurate at diagnosing mild steatosis and involves the use of radiation. Newer CT techniques, like dual energy CT, show potential in expanding the role of CT in quantifying steatosis. MRI proton-density fat fraction is currently the most accurate and precise imaging biomarker to quantify liver steatosis. As such, proton-density fat fraction is the most appropriate noninvasive end point for steatosis reduction in clinical trials and therapy response assessment.
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Affiliation(s)
- Yingzhen N Zhang
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
| | - Kathryn J Fowler
- Department of Radiology, Washington University School of Medicine, Washington University, St. Louis, MO, USA
| | - Gavin Hamilton
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
| | - Jennifer Y Cui
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
| | - Ethan Z Sy
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
| | - Michelle Balanay
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
| | - Jonathan C Hooker
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
| | - Nikolaus Szeverenyi
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
| | - Claude B Sirlin
- Department of Radiology, Liver Imaging Group, University of California San Diego, San Diego, CA, USA
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Li Q, Dhyani M, Grajo JR, Sirlin C, Samir AE. Current status of imaging in nonalcoholic fatty liver disease. World J Hepatol 2018; 10:530-542. [PMID: 30190781 PMCID: PMC6120999 DOI: 10.4254/wjh.v10.i8.530] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 06/25/2018] [Accepted: 06/28/2018] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most common diffuse liver disease, with a worldwide prevalence of 20% to 46%. NAFLD can be subdivided into simple steatosis and nonalcoholic steatohepatitis. Most cases of simple steatosis are non-progressive, whereas nonalcoholic steatohepatitis may result in chronic liver injury and progressive fibrosis in a significant minority. Effective risk stratification and management of NAFLD requires evaluation of hepatic parenchymal fat, fibrosis, and inflammation. Liver biopsy remains the current gold standard; however, non-invasive imaging methods are rapidly evolving and may replace biopsy in some circumstances. These methods include well-established techniques, such as conventional ultrasonography, computed tomography, and magnetic resonance imaging and newer imaging technologies, such as ultrasound elastography, quantitative ultrasound techniques, magnetic resonance elastography, and magnetic resonance-based fat quantitation techniques. The aim of this article is to review the current status of imaging methods for NAFLD risk stratification and management, including their diagnostic accuracy, limitations, and practical applicability.
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Affiliation(s)
- Qian Li
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Manish Dhyani
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
- Department of Radiology, Lahey Hospital and Medical Center, 41 Burlington Mall Road, Burlington, MA 01805, United States
| | - Joseph R Grajo
- Department of Radiology, Division of Abdominal Imaging, University of Florida College of Medicine, Gainesville, FL 32610, United States
| | - Claude Sirlin
- Altman Clinical Translational Research Institute, University of California, San Diego, CA 92103, United States
| | - Anthony E Samir
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
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Chartampilas E. Imaging of nonalcoholic fatty liver disease and its clinical utility. Hormones (Athens) 2018; 17:69-81. [PMID: 29858854 DOI: 10.1007/s42000-018-0012-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 12/04/2017] [Indexed: 12/21/2022]
Abstract
The prevalence of nonalcoholic fatty liver disease has been continuously rising over the last three decades and is projected to become the most common indication for liver transplantation in the near future. Its pathophysiology and complex interplay with diabetes and the metabolic syndrome are not as yet fully understood despite growing scientific interest and research. Modern imaging techniques offer significant assistance in this field by enabling the study of the liver noninvasively and evaluation of the degree of both steatosis and fibrosis, and even in attempting to diagnose the presence of inflammation (steatohepatitis). The derived measurements are highly precise, accurate and reproducible, performing better than biopsy in terms of quantification. In this article, these imaging techniques are overviewed and their performance regarding diagnosis, stratification and monitoring are evaluated. Their expanding role both in the research arena and in clinical practice along with their limitations is also discussed.
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Validation of goose liver fat measurement by QCT and CSE-MRI with biochemical extraction and pathology as reference. Eur Radiol 2017; 28:2003-2012. [PMID: 29238866 DOI: 10.1007/s00330-017-5189-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 11/05/2017] [Accepted: 11/09/2017] [Indexed: 12/19/2022]
Abstract
OBJECTIVES This study aimed to validate the accuracy and reliability of quantitative computed tomography (QCT) and chemical shift encoded magnetic resonance imaging (CSE-MRI) to assess hepatic steatosis. METHODS Twenty-two geese with a wide range of hepatic steatosis were collected. After QCT and CSE-MRI examinations, the liver of each goose was removed and samples were taken from the left lobe, upper and lower half of the right lobe for biochemical measurement and histology. Fat percentages by QCT and proton density fat fraction by MRI (MRI-PDFF) were measured within the sample regions of biochemical measurement and histology. The accuracy of QCT and MR measurements were assessed through Spearman correlation coefficients (r) and Passing and Bablok regression equations using biochemical measurement as the "gold standard". RESULTS Both QCT and MRI correlated highly with chemical extraction [r = 0.922 (p < 0.001) and r = 0.949 (p < 0.001) respectively]. Chemically extracted triglyceride was accurately predicted by both QCT liver fat percentages (Y = 0.6 + 0.866 × X) and by MRI-PDFF (Y = -1.8 + 0.773 × X). CONCLUSIONS QCT and CSE-MRI measurements of goose liver fat were accurate and reliable compared with biochemical measurement. KEY POINTS • QCT and CSE-MRI can measure liver fat content accurately and reliably • Histological grading of hepatic steatosis has larger sampling variability • QCT and CSE-MRI have potential in the clinical setting.
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Lee DH. Imaging evaluation of non-alcoholic fatty liver disease: focused on quantification. Clin Mol Hepatol 2017; 23:290-301. [PMID: 28994271 PMCID: PMC5760010 DOI: 10.3350/cmh.2017.0042] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 08/17/2017] [Indexed: 12/26/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) has been an emerging major health problem, and the most common cause of chronic liver disease in Western countries. Traditionally, liver biopsy has been gold standard method for quantification of hepatic steatosis. However, its invasive nature with potential complication as well as measurement variability are major problem. Thus, various imaging studies have been used for evaluation of hepatic steatosis. Ultrasonography provides fairly good accuracy to detect moderate-to-severe degree hepatic steatosis, but limited accuracy for mild steatosis. Operator-dependency and subjective/qualitative nature of examination are another major drawbacks of ultrasonography. Computed tomography can be considered as an unsuitable imaging modality for evaluation of NAFLD due to potential risk of radiation exposure and limited accuracy in detecting mild steatosis. Both magnetic resonance spectroscopy and magnetic resonance imaging using chemical shift technique provide highly accurate and reproducible diagnostic performance for evaluating NAFLD, and therefore, have been used in many clinical trials as a non-invasive reference of standard method.
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Affiliation(s)
- Dong Ho Lee
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
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Parakh A, Baliyan V, Sahani DV. Dual-Energy CT in Focal and Diffuse Liver Disease. CURRENT RADIOLOGY REPORTS 2017. [DOI: 10.1007/s40134-017-0226-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Patino M, Prochowski A, Agrawal MD, Simeone FJ, Gupta R, Hahn PF, Sahani DV. Material Separation Using Dual-Energy CT: Current and Emerging Applications. Radiographics 2017; 36:1087-105. [PMID: 27399237 DOI: 10.1148/rg.2016150220] [Citation(s) in RCA: 204] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Dual-energy (DE) computed tomography (CT) offers the opportunity to generate material-specific images on the basis of the atomic number Z and the unique mass attenuation coefficient of a particular material at different x-ray energies. Material-specific images provide qualitative and quantitative information about tissue composition and contrast media distribution. The most significant contribution of DE CT-based material characterization comes from the capability to assess iodine distribution through the creation of an image that exclusively shows iodine. These iodine-specific images increase tissue contrast and amplify subtle differences in attenuation between normal and abnormal tissues, improving lesion detection and characterization in the abdomen. In addition, DE CT enables computational removal of iodine influence from a CT image, generating virtual noncontrast images. Several additional materials, including calcium, fat, and uric acid, can be separated, permitting imaging assessment of metabolic imbalances, elemental deficiencies, and abnormal deposition of materials within tissues. The ability to obtain material-specific images from a single, contrast-enhanced CT acquisition can complement the anatomic knowledge with functional information, and may be used to reduce the radiation dose by decreasing the number of phases in a multiphasic CT examination. DE CT also enables generation of energy-specific and virtual monochromatic images. Clinical applications of DE CT leverage both material-specific images and virtual monochromatic images to expand the current role of CT and overcome several limitations of single-energy CT. (©)RSNA, 2016.
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Affiliation(s)
- Manuel Patino
- From the Division of Abdominal Imaging, Department of Radiology (M.P., A.P., M.D.A., F.J.S., R.G., D.V.S.), and Department of Abdominal Imaging and Intervention (P.F.H.), Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114
| | - Andrea Prochowski
- From the Division of Abdominal Imaging, Department of Radiology (M.P., A.P., M.D.A., F.J.S., R.G., D.V.S.), and Department of Abdominal Imaging and Intervention (P.F.H.), Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114
| | - Mukta D Agrawal
- From the Division of Abdominal Imaging, Department of Radiology (M.P., A.P., M.D.A., F.J.S., R.G., D.V.S.), and Department of Abdominal Imaging and Intervention (P.F.H.), Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114
| | - Frank J Simeone
- From the Division of Abdominal Imaging, Department of Radiology (M.P., A.P., M.D.A., F.J.S., R.G., D.V.S.), and Department of Abdominal Imaging and Intervention (P.F.H.), Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114
| | - Rajiv Gupta
- From the Division of Abdominal Imaging, Department of Radiology (M.P., A.P., M.D.A., F.J.S., R.G., D.V.S.), and Department of Abdominal Imaging and Intervention (P.F.H.), Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114
| | - Peter F Hahn
- From the Division of Abdominal Imaging, Department of Radiology (M.P., A.P., M.D.A., F.J.S., R.G., D.V.S.), and Department of Abdominal Imaging and Intervention (P.F.H.), Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114
| | - Dushyant V Sahani
- From the Division of Abdominal Imaging, Department of Radiology (M.P., A.P., M.D.A., F.J.S., R.G., D.V.S.), and Department of Abdominal Imaging and Intervention (P.F.H.), Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114
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Motosugi U, Hernando D, Wiens C, Bannas P, Reeder SB. High SNR Acquisitions Improve the Repeatability of Liver Fat Quantification Using Confounder-corrected Chemical Shift-encoded MR Imaging. Magn Reson Med Sci 2017; 16:332-339. [PMID: 28190853 PMCID: PMC5554738 DOI: 10.2463/mrms.mp.2016-0081] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To determine whether high signal-to-noise ratio (SNR) acquisitions improve the repeatability of liver proton density fat fraction (PDFF) measurements using confounder-corrected chemical shift-encoded magnetic resonance (MR) imaging (CSE-MRI). MATERIALS AND METHODS Eleven fat-water phantoms were scanned with 8 different protocols with varying SNR. After repositioning the phantoms, the same scans were repeated to evaluate the test-retest repeatability. Next, an in vivo study was performed with 20 volunteers and 28 patients scheduled for liver magnetic resonance imaging (MRI). Two CSE-MRI protocols with standard- and high-SNR were repeated to assess test-retest repeatability. MR spectroscopy (MRS)-based PDFF was acquired as a standard of reference. The standard deviation (SD) of the difference (Δ) of PDFF measured in the two repeated scans was defined to ascertain repeatability. The correlation between PDFF of CSE-MRI and MRS was calculated to assess accuracy. The SD of Δ and correlation coefficients of the two protocols (standard- and high-SNR) were compared using F-test and t-test, respectively. Two reconstruction algorithms (complex-based and magnitude-based) were used for both the phantom and in vivo experiments. RESULTS The phantom study demonstrated that higher SNR improved the repeatability for both complex- and magnitude-based reconstruction. Similarly, the in vivo study demonstrated that the repeatability of the high-SNR protocol (SD of Δ = 0.53 for complex- and = 0.85 for magnitude-based fit) was significantly higher than using the standard-SNR protocol (0.77 for complex, P < 0.001; and 0.94 for magnitude-based fit, P = 0.003). No significant difference was observed in the accuracy between standard- and high-SNR protocols. CONCLUSION Higher SNR improves the repeatability of fat quantification using confounder-corrected CSE-MRI.
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Affiliation(s)
- Utaroh Motosugi
- Department of Radiology, University of Wisconsin.,Department of Radiology, University of Yamanashi
| | | | - Curtis Wiens
- Department of Radiology, University of Wisconsin
| | - Peter Bannas
- Department of Radiology, University of Wisconsin.,Department of Radiology, University Hospital Hamburg-Eppendorf
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin.,Department of Biomedical Engineering, University of Wisconsin.,Department of Medical Physics, University of Wisconsin.,Department of Medicine, University of Wisconsin.,Department of Emergency Medicine, University of Wisconsin
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White Paper of the Society of Computed Body Tomography and Magnetic Resonance on Dual-Energy CT, Part 4: Abdominal and Pelvic Applications. J Comput Assist Tomogr 2017; 41:8-14. [PMID: 27824670 DOI: 10.1097/rct.0000000000000546] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This is the fourth of a series of 4 white papers that represent expert consensus documents developed by the Society of Computed Body Tomography and Magnetic Resonance through its task force on dual-energy computed tomography. This article, part 4, discusses DECT for abdominal and pelvic applications and, at the end of each, will offer our consensus opinions on the current clinical utility of the application and opportunities for further research.
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Idilman IS, Ozdeniz I, Karcaaltincaba M. Hepatic Steatosis: Etiology, Patterns, and Quantification. Semin Ultrasound CT MR 2016; 37:501-510. [DOI: 10.1053/j.sult.2016.08.003] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Accuracy of Liver Fat Quantification With Advanced CT, MRI, and Ultrasound Techniques: Prospective Comparison With MR Spectroscopy. AJR Am J Roentgenol 2016; 208:92-100. [PMID: 27726414 DOI: 10.2214/ajr.16.16565] [Citation(s) in RCA: 176] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE The purpose of this study was to prospectively evaluate the accuracy of proton-density fat-fraction, single- and dual-energy CT (SECT and DECT), gray-scale ultrasound (US), and US shear-wave elastography (US-SWE) in the quantification of hepatic steatosis with MR spectroscopy (MRS) as the reference standard. SUBJECTS AND METHODS Fifty adults who did not have symptoms (23 men, 27 women; mean age, 57 ± 5 years; body mass index, 27 ± 5) underwent liver imaging with un-enhanced SECT, DECT, gray-scale US, US-SWE, proton-density fat-fraction MRI, and MRS for this prospective trial. MRS voxels for the reference standard were colocalized with all other modalities under investigation. For SECT (120 kVp), attenuation values were recorded. For rapid-switching DECT (80/140 kVp), monochromatic images (70-140 keV) and fat density-derived material decomposition images were reconstructed. For proton-density fat fraction MRI, a quantitative chemical shift-encoded method was used. For US, echogenicity was evaluated on a qualitative 0-3 scale. Quantitative US shear-wave velocities were also recorded. Data were analyzed by linear regression for each technique compared with MRS. RESULTS There was excellent correlation between MRS and both proton-density fat-fraction MRI (r2 = 0.992; slope, 0.974; intercept, -0.943) and SECT (r2 = 0.856; slope, -0.559; intercept, 35.418). DECT fat attenuation had moderate correlation with MRS measurements (r2 = 0.423; slope, 0.034; intercept, 8.459). There was good correlation between qualitative US echogenicity and MRS measurements with a weighted kappa value of 0.82. US-SWE velocity did not have reliable correlation with MRS measurements (r2 = 0.004; slope, 0.069; intercept, 6.168). CONCLUSION Quantitative MRI proton-density fat fraction and SECT fat attenuation have excellent linear correlation with MRS measurements and can serve as accurate noninvasive biomarkers for quantifying steatosis. Material decomposition with DECT does not improve the accuracy of fat quantification over conventional SECT attenuation. US-SWE has poor accuracy for liver fat quantification.
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Graffy PM, Pickhardt PJ. Quantification of hepatic and visceral fat by CT and MR imaging: relevance to the obesity epidemic, metabolic syndrome and NAFLD. Br J Radiol 2016; 89:20151024. [PMID: 26876880 PMCID: PMC5258166 DOI: 10.1259/bjr.20151024] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 02/09/2016] [Accepted: 02/11/2016] [Indexed: 02/06/2023] Open
Abstract
Trends in obesity have continued to increase in the developed world over the past few decades, along with related conditions such as metabolic syndrome, which is strongly associated with this epidemic. Novel and innovative methods to assess relevant obesity-related biomarkers are needed to determine the clinical significance, allow for surveillance and intervene if appropriate. Aggregations of specific types of fat, specifically hepatic and visceral adiposity, are now known to be correlated with these conditions, and there are a variety of imaging techniques to identify and quantify their distributions and provide diagnostic information. These methods are particularly salient for metabolic syndrome, which is related to both hepatic and visceral adiposity but currently not defined by it. Simpler non-specific fat measurements, such as body weight, abdominal circumference and body mass index are more frequently used but lack the ability to characterize fat location. In addition, non-alcoholic fatty liver disease (NAFLD) is a related condition that carries relevance not only for obesity-related diseases but also for the progression of the liver-specific disease, including non-alcoholic steatohepatitis and cirrhosis, albeit at a much lower frequency. Recent CT and MRI techniques have emerged to potentially optimize diagnosing metabolic syndrome and NAFLD through non-invasive quantification of visceral fat and hepatic steatosis with high accuracy. These imaging modalities should aid us in further understanding the relationship of hepatic and visceral fat to the obesity-related conditions such as metabolic syndrome, NAFLD and cardiovascular disease.
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Affiliation(s)
- Peter M Graffy
- Department of Radiology, University of Wisconsin School of Medicine
& Public Health, Madison, WI
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine
& Public Health, Madison, WI
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Kim S, Shuman WP. Clinical Applications of Dual-Energy Computed Tomography in the Liver. Semin Roentgenol 2016; 51:284-291. [PMID: 27743564 DOI: 10.1053/j.ro.2016.05.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Sooah Kim
- Department of Radiology, University of Washington, Seattle, WA.
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Abstract
Conventional imaging modalities, including ultrasonography (US), computed tomography (CT), and magnetic resonance (MR), play an important role in the diagnosis and management of patients with nonalcoholic fatty liver disease (NAFLD) by allowing noninvasive diagnosis of hepatic steatosis. However, conventional imaging modalities are limited as biomarkers of NAFLD for various reasons. Multi-parametric quantitative MRI techniques overcome many of the shortcomings of conventional imaging and allow comprehensive and objective evaluation of NAFLD. MRI can provide unconfounded biomarkers of hepatic fat, iron, and fibrosis in a single examination-a virtual biopsy has become a clinical reality. In this article, we will review the utility and limitation of conventional US, CT, and MR imaging for the diagnosis NAFLD. Recent advances in imaging biomarkers of NAFLD are also discussed with an emphasis in multi-parametric quantitative MRI.
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Affiliation(s)
- Sonja Kinner
- Department of Radiology, University of Wisconsin, Madison, WI, USA
- Department of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Scott B Reeder
- Department of Radiology, Medical Physics, Biomedical Engineering, Medicine, Emergency Medicine, University of Wisconsin, Madison, WI, USA
| | - Takeshi Yokoo
- Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, 2201 Inwood Road, NE2.210B, Dallas, TX, 75390-9085, USA.
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