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Chung KJ, Chaudhari AJ, Nardo L, Jones T, Chen MS, Badawi RD, Cherry SR, Wang G. Quantitative Total-Body Imaging of Blood Flow with High-Temporal-Resolution Early Dynamic 18F-FDG PET Kinetic Modeling. J Nucl Med 2025:jnumed.124.268706. [PMID: 40306973 DOI: 10.2967/jnumed.124.268706] [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: 09/07/2024] [Accepted: 04/08/2025] [Indexed: 05/02/2025] Open
Abstract
Past efforts to measure blood flow with the widely available radiotracer 18F-FDG were limited to tissues with high 18F-FDG extraction fraction. In this study, we developed an early dynamic 18F-FDG PET method with high-temporal-resolution (HTR) kinetic modeling to assess total-body blood flow based on deriving the vascular phase of 18F-FDG transit and conducted a pilot comparison study against a 11C-butanol flow-tracer reference. Methods: The first 2 min of dynamic PET scans were reconstructed at HTR (60 × 1 s/frame, 30 × 2 s/frame) to resolve the rapid passage of the radiotracer through blood vessels. In contrast to existing methods that use blood-to-tissue transport rate as a surrogate of blood flow, our method directly estimated blood flow using a distributed kinetic model (adiabatic approximation to tissue homogeneity [AATH] model). To validate our 18F-FDG measurements of blood flow against a reference flow-specific radiotracer, we analyzed total-body dynamic PET images of 6 human participants scanned with both 18F-FDG and 11C-butanol. An additional 34 total-body dynamic 18F-FDG PET images of healthy participants were analyzed for comparison against published blood-flow ranges. Regional blood flow was estimated across the body, and total-body parametric imaging of blood flow was conducted for visual assessment. AATH and standard compartment model fitting was compared using the Akaike information criterion at different temporal resolutions. Results: 18F-FDG blood flow was in quantitative agreement with flow measured from 11C-butanol across same-subject regional measurements (Pearson correlation coefficient, 0.955; P < 0.001; linear regression slope and intercept, 0.973 and -0.012, respectively), which was visually corroborated by total-body blood-flow parametric imaging. Our method resolved a wide range of blood-flow values across the body in broad agreement with published ranges (e.g., healthy cohort values of 0.51 ± 0.12 mL/min/cm3 in the cerebral cortex and 2.03 ± 0.64 mL/min/cm3 in the lungs). HTR (1-2 s/frame) was required for AATH modeling. Conclusion: Total-body blood-flow imaging was feasible using early dynamic 18F-FDG PET with HTR kinetic modeling. This method may be combined with standard 18F-FDG PET methods to enable efficient single-tracer multiparametric flow-metabolism imaging, with numerous research and clinical applications in oncology, cardiovascular disease, pain medicine, and neuroscience.
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Affiliation(s)
- Kevin J Chung
- Department of Radiology, University of California Davis Health, Sacramento, California;
| | - Abhijit J Chaudhari
- Department of Radiology, University of California Davis Health, Sacramento, California
| | - Lorenzo Nardo
- Department of Radiology, University of California Davis Health, Sacramento, California
| | - Terry Jones
- Department of Radiology, University of California Davis Health, Sacramento, California
| | - Moon S Chen
- Department of Internal Medicine, University of California Davis Health, Sacramento, California; and
| | - Ramsey D Badawi
- Department of Radiology, University of California Davis Health, Sacramento, California
- Department of Biomedical Engineering, University of California at Davis, Davis, California
| | - Simon R Cherry
- Department of Radiology, University of California Davis Health, Sacramento, California
- Department of Biomedical Engineering, University of California at Davis, Davis, California
| | - Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, California
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Mertens AJ, Cheng HLM. Accelerated dynamic magnetic resonance imaging from Spatial-Subspace Reconstructions (SPARS). PLoS One 2025; 20:e0317271. [PMID: 39888888 PMCID: PMC11785264 DOI: 10.1371/journal.pone.0317271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 12/24/2024] [Indexed: 02/02/2025] Open
Abstract
Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) ideally requires a high spatial and a high temporal resolution, but hardware limitations prevent acquisitions from achieving both simultaneously-either high temporal resolution is exchanged for spatial resolution, or vice versa. Even state-of-the-art image reconstruction techniques that infer missing data in a sparse acquisition space cannot recover the loss of spatial detail, especially at high temporal acceleration rates. The purpose of this paper is to introduce the concept of spatial subspace reconstructions (SPARS) and demonstrate its ability to reconstruct high spatial resolution dynamic images from as few as one acquired k-space spoke per time frame in a dynamic series. Briefly, a low-temporal-high-spatial resolution organization of the acquired raw data is used to estimate the basis vectors of the spatial subspace in which the high-temporal-high-spatial ground truth data resides. This subspace is then used to estimate entire images from single k-space spokes. In both simulated and human in-vivo data, the proposed SPARS reconstruction method outperformed standard GRASP and GRASP-Pro reconstruction, providing a shorter reconstruction time and yielding higher accuracy from both a spatial and temporal perspective.
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Affiliation(s)
- Alexander J. Mertens
- The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
- Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program, Toronto, Canada
| | - Hai-Ling Margaret Cheng
- The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
- Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
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Zhou M, Huang H, Gong T, Chen M. The application of the golden-angle radial sparse parallel technique in T restaging of locally advanced rectal cancer after neoadjuvant chemoradiotherapy. Abdom Radiol (NY) 2024; 49:2960-2970. [PMID: 38822854 DOI: 10.1007/s00261-024-04400-x] [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: 02/14/2024] [Revised: 05/07/2024] [Accepted: 05/12/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE To evaluate the diagnostic performance of Golden-Angle Radial Sparse Parallel (GRASP) MRI in identifying pathological stage T0-1 (ypT0-1) after neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer, compared to T2-weighted imaging (T2WI) combined with Diffusion Weighted Imaging (DWI). METHODS In this retrospective study, 168 patients were carefully selected based on inclusion criteria that targeted individuals with biopsy-confirmed primary rectal adenocarcinoma, identified via MRI as having locally advanced disease (≥ T3 and/or positive lymph node results) prior to nCRT. Post-nCRT, all MRI images obtained after nCRT were assessed by two observers independently. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for identifying ypT0-1 based on GRASP and T2 + DWI were calculated. Multivariable regression analysis was used to explore the factors independently associated with ypT0-1 tumor. RESULTS 45 patients out of these cases were ypT0-1, and the accuracy, sensitivity, specificity, PPV, and NPV of GRASP were higher than the T2 + DWI (88% vs 74%, 93% vs 71%, 86% vs 75%, 71% vs 52% and 97% vs 88%), the AUC in identifying ypT0-1 tumor based on GRASP was 0.90 (95% CI:0.84, 0.94), which was better than the T2 + DWI (0.73; 95% CI: 0.66, 0.80). Multivariable logistic regression analysis showed that the yT stage on GRASP scans was the only factor independently associated with ypT0-1 tumor (P < 0.001). CONCLUSION The GRASP helped distinguish ypT0-1 tumor after nCRT and can select patients who may be suitable for local excision.
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Affiliation(s)
- Mi Zhou
- Department of Radiology, Sichuan Provincial Orthpaedics Hospital, Chengdu, 610041, People's Republic of China.
| | - Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
| | - Tong Gong
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, 200135, People's Republic of China
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Smits HJG, Bennink E, Ruiter LN, Breimer GE, Willems SM, Dankbaar JW, Philippens MEP. Spatial correlation between in vivo imaging and immunohistochemical biomarkers: A methodological study. Transl Oncol 2024; 48:102051. [PMID: 39018773 DOI: 10.1016/j.tranon.2024.102051] [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: 01/24/2024] [Revised: 05/09/2024] [Accepted: 07/01/2024] [Indexed: 07/19/2024] Open
Abstract
In this study, we present a method that enables voxel-by-voxel comparison of in vivo imaging to immunohistochemistry (IHC) biomarkers. As a proof of concept, we investigated the spatial correlation between dynamic contrast enhanced (DCE-)CT parameters and IHC biomarkers Ki-67 (proliferation), HIF-1α (hypoxia), and CD45 (immune cells). 54 whole-mount tumor slices of 15 laryngeal and hypopharyngeal carcinomas were immunohistochemically stained and digitized. Heatmaps of biomarker positivity were created and registered to DCE-CT parameter maps. The adiabatic approximation to the tissue homogeneity model was used to fit the following DCE parameters: Ktrans (transfer constant), Ve (extravascular and extracellular space), and Vi (intravascular space). Both IHC and DCE maps were downsampled to 4 × 4 × 3 mm[3] voxels. The mean values per tumor were used to calculate the between-subject correlations between parameters. For the within-subject (spatial) correlation, values of all voxels within a tumor were compared using the repeated measures correlation (rrm). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: Ve and Ki-67 (rrm = -0.17, P < .001), Ve and HIF-1α (rrm = -0.12, P < .001), Ktrans and CD45 (rrm = 0.13, P < .001), Vi and CD45 (rrm = 0.16, P < .001), and Vi and Ki-67 (rrm = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (rrm = 0.35, P < .001). This study shows the technical feasibility of determining the 3 dimensional spatial correlation between histopathological biomarker heatmaps and in vivo imaging. It also shows that between-subject correlations do not reflect within-subject correlations of parameters.
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Affiliation(s)
- Hilde J G Smits
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Edwin Bennink
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Lilian N Ruiter
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerben E Breimer
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Stefan M Willems
- Department of Pathology and Medical Biology, University Medical Center Groningen, Groningen, the Netherlands
| | - Jan W Dankbaar
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
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Xie T, Zhao Q, Fu C, Grimm R, Dominik Nickel M, Hu X, Yue L, Peng W, Gu Y. Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis. Eur J Radiol 2024; 176:111501. [PMID: 38788607 DOI: 10.1016/j.ejrad.2024.111501] [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: 05/22/2023] [Revised: 04/27/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024]
Abstract
PURPOSE To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. METHODS This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. RESULTS kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). CONCLUSION The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Xiaoxin Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Lei Yue
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
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Kratochvíla J, Jiřík R, Bartoš M, Standara M, Starčuk Z, Taxt T. Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling. Magn Reson Imaging 2024; 109:238-248. [PMID: 38508292 DOI: 10.1016/j.mri.2024.03.019] [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: 08/07/2023] [Revised: 03/08/2024] [Accepted: 03/16/2024] [Indexed: 03/22/2024]
Abstract
PURPOSE Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied. METHODS The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution. RESULTS The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s. CONCLUSION We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.
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Affiliation(s)
- Jiří Kratochvíla
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic.
| | - Radovan Jiřík
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic
| | - Michal Bartoš
- Czech Academy of Sciences, Institute of Information Technology and Automation, Pod Vodárenskou věží 4, 182 08 Praha 8, Czech Republic
| | - Michal Standara
- Department of Radiology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic
| | - Zenon Starčuk
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic
| | - Torfinn Taxt
- Department of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen, Norway
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Bhandari A, Gu B, Kashkooli FM, Zhan W. Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy. J Control Release 2024; 370:721-746. [PMID: 38718876 DOI: 10.1016/j.jconrel.2024.05.004] [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: 02/04/2024] [Revised: 04/11/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.
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Affiliation(s)
- Ajay Bhandari
- Biofluids Research Lab, Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India
| | - Boram Gu
- School of Chemical Engineering, Chonnam National University, Gwangju, Republic of Korea
| | | | - Wenbo Zhan
- School of Engineering, University of Aberdeen, Aberdeen, UK.
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Abstract
The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.
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Affiliation(s)
- Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - James H Holmes
- Radiology, Biomedical Engineering, and Holden Cancer Center, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA.
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Wu C, Wang N, Gaddam S, Wang L, Han H, Sung K, Christodoulou AG, Xie Y, Pandol S, Li D. Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study. FRONTIERS IN RADIOLOGY 2023; 3:1168901. [PMID: 37731600 PMCID: PMC10507354 DOI: 10.3389/fradi.2023.1168901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/24/2023] [Indexed: 09/22/2023]
Abstract
Introduction Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated. Method Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well. Results The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant K trans , fractional extravascular extracellular volume v e , and rate constant k ep ) achieved intraclass correlation coefficient and R2 between 0.84-0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas. Discussion Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.
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Affiliation(s)
- Chaowei Wu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Radiology Department, Stanford University, Stanford, CA, United States
| | - Srinivas Gaddam
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Lixia Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Kyunghyun Sung
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Stephen Pandol
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
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Ren Z, Easley TO, Pineda FD, Guo X, Barber RF, Karczmar GS. Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI. PLoS One 2023; 18:e0286123. [PMID: 37319275 PMCID: PMC10270582 DOI: 10.1371/journal.pone.0286123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
The high spatial and temporal resolution of dynamic contrast-enhanced MRI (DCE-MRI) can improve the diagnostic accuracy of breast cancer screening in patients who have dense breasts or are at high risk of breast cancer. However, the spatiotemporal resolution of DCE-MRI is limited by technical issues in clinical practice. Our earlier work demonstrated the use of image reconstruction with enhancement-constrained acceleration (ECA) to increase temporal resolution. ECA exploits the correlation in k-space between successive image acquisitions. Because of this correlation, and due to the very sparse enhancement at early times after contrast media injection, we can reconstruct images from highly under-sampled k-space data. Our previous results showed that ECA reconstruction at 0.25 seconds per image (4 Hz) can estimate bolus arrival time (BAT) and initial enhancement slope (iSlope) more accurately than a standard inverse fast Fourier transform (IFFT) when k-space data is sampled following a Cartesian based sampling trajectory with adequate signal-to-noise ratio (SNR). In this follow-up study, we investigated the effect of different Cartesian based sampling trajectories, SNRs and acceleration rates on the performance of ECA reconstruction in estimating contrast media kinetics in lesions (BAT, iSlope and Ktrans) and in arteries (Peak signal intensity of first pass, time to peak, and BAT). We further validated ECA reconstruction with a flow phantom experiment. Our results show that ECA reconstruction of k-space data acquired with 'Under-sampling with Repeated Advancing Phase' (UnWRAP) trajectories with an acceleration factor of 14, and temporal resolution of 0.5 s/image and high SNR (SNR ≥ 30 dB, noise standard deviation (std) < 3%) ensures minor errors (5% or 1 s error) in lesion kinetics. Medium SNR (SNR ≥ 20 dB, noise std ≤ 10%) was needed to accurately measure arterial enhancement kinetics. Our results also suggest that accelerated temporal resolution with ECA with 0.5 s/image is practical.
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Affiliation(s)
- Zhen Ren
- Department of Radiology, The University of Chicago, Chicago, Illinois, United States of America
| | - Ty O. Easley
- McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Federico D. Pineda
- Department of Radiology, The University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Xiaodong Guo
- Department of Radiology, The University of Chicago, Chicago, Illinois, United States of America
| | - Rina F. Barber
- Department of Statistics, The University of Chicago, Chicago, Illinois, United States of America
| | - Gregory S. Karczmar
- Department of Radiology, The University of Chicago, Chicago, Illinois, United States of America
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Sinno N, Taylor E, Hompland T, Milosevic M, Jaffray DA, Coolens C. Incorporating cross-voxel exchange for the analysis of dynamic contrast-enhanced imaging data: pre-clinical results. Phys Med Biol 2022; 67. [PMID: 36541560 DOI: 10.1088/1361-6560/aca512] [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: 12/10/2021] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Tumours exhibit abnormal interstitial structures and vasculature function often leading to impaired and heterogeneous drug delivery. The disproportionate spatial accumulation of a drug in the interstitium is determined by several microenvironmental properties (blood vessel distribution and permeability, gradients in the interstitial fluid pressure). Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the quantification of tracer perfusion based on contrast enhancement measured with non-invasive imaging techniques. An advanced cross-voxel exchange model (CVXM) was recently developed to provide a comprehensive description of tracer extravasation as well as advection and diffusion based on cross-voxel tracer kinetics (Sinnoet al2021). Transport parameters were derived from DCE-MRI of twenty TS-415 human cervical carcinoma xenografts by using CVXM. Tracer velocity flows were measured at the tumour periphery (mean 1.78-5.82μm.s-1) pushing the contrast outward towards normal tissue. These elevated velocity measures and extravasation rates explain the heterogeneous distribution of tracer across the tumour and its accumulation at the periphery. Significant values for diffusivity were deduced across the tumours (mean 152-499μm2.s-1). CVXM resulted in generally smaller values for the extravasation parameterKext(mean 0.01-0.04 min-1) and extravascular extracellular volume fractionve(mean 0.05-0.17) compared to the standard Tofts parameters, suggesting that Toft model underestimates the effects of inter-voxel exchange. The ratio of Tofts' extravasation parameters over CVXM's was significantly positively correlated to the cross-voxel diffusivity (P< 0.0001) and velocity (P= 0.0005). Tofts' increasedvemeasurements were explained using Sinnoet al(2021)'s theoretical work. Finally, a scan time of 15 min renders informative estimations of the transport parameters. However, a duration as low as 7.5 min is acceptable to recognize the spatial variation of transport parameters. The results demonstrate the potential of utilizing CVXM for determining metrics characterizing the exchange of tracer between the vasculature and the tumour tissue. Like for many earlier models, additional work is strongly recommended, in terms of validation, to develop more confidence in the results, motivating future laboratory work in this regard.
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Affiliation(s)
- Noha Sinno
- The Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada.,The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Edward Taylor
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada
| | - Tord Hompland
- Department of Radiation Biology, Oslo University Hospital, Oslo, Norway
| | - Michael Milosevic
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Institute of Medical Science, University of Toronto, Toronto, Canada
| | - David A Jaffray
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,University of Texas, MD Anderson Cancer Centre, Texas, United States of America
| | - Catherine Coolens
- The Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada.,The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
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12
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Milidonis X, Nazir MS, Chiribiri A. Impact of Temporal Resolution and Methods for Correction on Cardiac Magnetic Resonance Perfusion Quantification. J Magn Reson Imaging 2022; 56:1707-1719. [PMID: 35338754 PMCID: PMC9790572 DOI: 10.1002/jmri.28180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Acquisition of magnetic resonance first-pass perfusion images is synchronized to the patient's heart rate (HR) and governs the temporal resolution. This is inherently linked to the process of myocardial blood flow (MBF) quantification and impacts MBF accuracy but to an unclear extent. PURPOSE To assess the impact of temporal resolution on quantitative perfusion and compare approaches for accounting for its variability. STUDY TYPE Prospective phantom and retrospective clinical study. POPULATION AND PHANTOM Simulations, a cardiac perfusion phantom, and 30 patients with (16, 53%) or without (14, 47%) coronary artery disease. FIELD STRENGTH/SEQUENCE 3.0 T/2D saturation recovery spoiled gradient echo sequence. ASSESSMENT Dynamic perfusion data were simulated for a range of reference MBF (1 mL/g/min-5 mL/g/min) and HR (30 bpm-150 bpm). Perfusion imaging was performed in patients and a phantom for different temporal resolutions. MBF and myocardial perfusion reserve (MPR) were quantified without correction for temporal resolution or following correction by either MBF scaling based on the sampling interval or data interpolation prior to quantification. Simulated data were quantified using Fermi deconvolution, truncated singular value decomposition, and one-compartment modeling, whereas phantom and clinical data were quantified using Fermi deconvolution alone. STATISTICAL TESTS Shapiro-Wilk tests for normality, percentage error (PE) for measuring MBF accuracy in simulations, and one-way repeated measures analysis of variance with Bonferroni correction to compare clinical MBF and MPR. Statistical significance set at P < 0.05. RESULTS For Fermi deconvolution and an example simulated 1 mL/g/min, the MBF PE without correction for temporal resolution was between 55.4% and -62.7% across 30-150 bpm. PE was between -22.2% and -6.8% following MBF scaling and between -14.2% and -14.2% following data interpolation across the same HR. An interpolated HR of 240 bpm reduced PE to ≤10%. Clinical rest and stress MBF and MPR were significantly different between analyses. DATA CONCLUSION Accurate perfusion quantification needs to account for the variability of temporal resolution, with data interpolation prior to quantification reducing MBF variability across different resolutions. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Xenios Milidonis
- School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | | | - Amedeo Chiribiri
- School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
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13
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Park JS, Choi SH, Sohn CH, Park J. Joint Reconstruction of Vascular Structure and Function Maps in Dynamic Contrast Enhanced MRI Using Vascular Heterogeneity Priors. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:52-62. [PMID: 34379591 DOI: 10.1109/tmi.2021.3104016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This work introduces a novel, joint reconstruction of vascular structure and microvascular function maps directly from highly undersampled data in k - t space using vascular heterogeneity priors for high-definition, dynamic contrast-enhanced (DCE) MRI. In DCE MRI, arteries and veins are characterized by rapid, high uptake and wash-out of contrast agents (CA). On the other hand, depending on CA uptake and wash-out signal patterns, capillary tissues can be categorized into highly perfused, moderately perfused, and necrotic regions. Given the above considerations, macrovascular maps are generated as a prior to differentiate penalties on arteries relative to capillary tissues during image reconstruction. Furthermore, as a microvascular prior, contrast dynamics in capillary regions are represented in a low dimensional space using a finite number of basic vectors that reflect actual tissue-specific signal patterns. Both vascular structure and microvascular function maps are jointly estimated by solving a constrained optimization problem in which the above vascular heterogeneity priors are represented by spatially weighted nonnegative matrix factorization. Retrospective and prospective experiments are performed to validate the effectiveness of the proposed method in generating well-defined vascular structure and microvascular function maps for patients with brain tumor at high reduction factors.
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14
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Easley TO, Ren Z, Kim B, Karczmar GS, Barber RF, Pineda FD. Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI. PLoS One 2021; 16:e0258621. [PMID: 34710110 PMCID: PMC8553053 DOI: 10.1371/journal.pone.0258621] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 10/01/2021] [Indexed: 02/08/2023] Open
Abstract
In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.
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Affiliation(s)
- Ty O. Easley
- McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Zhen Ren
- Department of Radiology, University of Chicago, Chicago, Illinois, United States of America
| | - Byol Kim
- Department of Biostatistics at the University of Washington, Seattle, Washington, United States of America
| | - Gregory S. Karczmar
- Department of Radiology, University of Chicago, Chicago, Illinois, United States of America
| | - Rina F. Barber
- Department of Statistics, University of Chicago, Chicago, Illinois, United States of America
| | - Federico D. Pineda
- Department of Radiology, University of Chicago, Chicago, Illinois, United States of America
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15
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Wu C, Hormuth DA, Easley T, Eijkhout V, Pineda F, Karczmar GS, Yankeelov TE. An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom. Med Image Anal 2021; 73:102186. [PMID: 34329903 PMCID: PMC8453106 DOI: 10.1016/j.media.2021.102186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 07/08/2021] [Accepted: 07/16/2021] [Indexed: 10/20/2022]
Abstract
Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the "ground truth" data to which the output of a given processing method must be compared. One way to address this issue is via "digital phantoms", which are numerical models that provide known biophysical properties of a particular object of interest. In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SRA, where the subscript indicates Protocol A) or signal-to-noise ratio (SNRA) decreases. Specifically, with an SNRA / SRA = 75 dB / 30 μm, the median CNR is 77.30, whereas an SNRA / SRA = 5 dB / 300 μm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PESER). We find that PESER significantly decreases the as temporal resolution (TRB) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PESER. For example, with an SNRB / SRB / TRB = 5 dB / 300 μm / 10 s, the median PESER is 21.00%, whereas an SNRB / SRB / TRB = 75 dB / 60 μm / 1 s, yields a median PESER of 0.90%. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States.
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States; Livestrong Cancer Institutes, United States
| | - Ty Easley
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
| | | | - Federico Pineda
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Gregory S Karczmar
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States; Livestrong Cancer Institutes, United States; Departments of Biomedical Engineering, United States; Departments of Diagnostic Medicine, United States; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, United States; Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77030, United States
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16
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Shah AD, Shridhar Konar A, Paudyal R, Oh JH, LoCastro E, Nuñez DA, Swinburne N, Vachha B, Ulaner GA, Young RJ, Holodny AI, Beal K, Shukla-Dave A, Hatzoglou V. Diffusion and Perfusion MRI Predicts Response Preceding and Shortly After Radiosurgery to Brain Metastases: A Pilot Study. J Neuroimaging 2020; 31:317-323. [PMID: 33370467 DOI: 10.1111/jon.12828] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/20/2020] [Accepted: 12/06/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND AND PURPOSE To determine the ability of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict long-term response of brain metastases prior to and within 72 hours of stereotactic radiosurgery (SRS). METHODS In this prospective pilot study, multiple b-value DWI and T1-weighted DCE-MRI were performed in patients with brain metastases before and within 72 hours following SRS. Diffusion-weighted images were analyzed using the monoexponential and intravoxel incoherent motion (IVIM) models. DCE-MRI data were analyzed using the extended Tofts pharmacokinetic model. The parameters obtained with these methods were correlated with brain metastasis outcomes according to modified Response Assessment in Neuro-Oncology Brain Metastases criteria. RESULTS We included 25 lesions from 16 patients; 16 patients underwent pre-SRS MRI and 12 of 16 patients underwent both pre- and early (within 72 hours) post-SRS MRI. The perfusion fraction (f) derived from IVIM early post-SRS was higher in lesions demonstrating progressive disease than in lesions demonstrating stable disease, partial response, or complete response (q = .041). Pre-SRS extracellular extravascular volume fraction, ve , and volume transfer coefficient, Ktrans , derived from DCE-MRI were higher in nonresponders versus responders (q = .041). CONCLUSIONS Quantitative DWI and DCE-MRI are feasible imaging methods in the pre- and early (within 72 hours) post-SRS evaluation of brain metastases. DWI- and DCE-MRI-derived parameters demonstrated physiologic changes (tumor cellularity and vascularity) and offer potentially useful biomarkers that can predict treatment response. This allows for initiation of alternate therapies within an effective time window that may help prevent disease progression.
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Affiliation(s)
- Akash Deelip Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - David Aramburu Nuñez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Nathaniel Swinburne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gary A Ulaner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kathryn Beal
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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17
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Paudyal R, Lu Y, Hatzoglou V, Moreira A, Stambuk HE, Oh JH, Cunanan KM, Nunez DA, Mazaheri Y, Gonen M, Ho A, Fagin JA, Wong RJ, Shaha A, Tuttle RM, Shukla-Dave A. Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers. NMR IN BIOMEDICINE 2020; 33:e4166. [PMID: 31680360 PMCID: PMC7687051 DOI: 10.1002/nbm.4166] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 07/04/2019] [Accepted: 07/17/2019] [Indexed: 06/10/2023]
Abstract
The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yonggang Lu
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Andre Moreira
- Department of Pathology, NYU Langone Medical Center, New
York, USA
| | - Hilda E. Stambuk
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Kristen M. Cunanan
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - David Aramburu Nunez
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - Alan Ho
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - James A. Fagin
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Ashok Shaha
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - R. Michael Tuttle
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
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18
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Park JS, Lim E, Choi SH, Sohn CH, Lee J, Park J. Model-Based High-Definition Dynamic Contrast Enhanced MRI for Concurrent Estimation of Perfusion and Microvascular Permeability. Med Image Anal 2019; 59:101566. [PMID: 31639623 DOI: 10.1016/j.media.2019.101566] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 09/20/2019] [Accepted: 09/26/2019] [Indexed: 01/18/2023]
Abstract
This work introduces a model-based, high-definition dynamic contrast enhanced (DCE) MRI for concurrent estimation of perfusion and microvascular permeability over the whole brain. A time series of reference-subtracted signals is decomposed into one component that reflects main contrast dynamics and the other one that includes residual contrast agents (CA) and background signals. The former is described by linear superposition of a finite number of basic vectors trained from an augmented set of data that consists of tracer-kinetic model driven signal vectors and patient-specific measured ones. Contrast dynamics is estimated by solving a constrained optimization problem that incorporates the linearized signal decomposition into the measurement model of DCE MRI and then combining the main component with the background-suppressed, residual CA signals. To the best of our knowledge, this is the first work that prospectively enables rapid temporal sampling with 1.5 s (3 ∼ 4 times higher than clinical routines) while simultaneously achieving high isotropic spatial resolution with 1.0 mm3 (4 ∼ 6 times higher than routines), enhancing estimation of both patient-specific inputs and outputs for quantification of microvascular functions. Simulations and experiments are performed to demonstrate the effectiveness of the proposed method in patients with brain cancer.
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Affiliation(s)
- Joon Sik Park
- Department of Biomedical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon, Republic of Korea
| | - Eunji Lim
- Department of Biomedical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon, Republic of Korea
| | - Seung-Hong Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Joonyeol Lee
- Department of Biomedical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Jaeseok Park
- Department of Biomedical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon, Republic of Korea; Biomedical Institute for Convergence, Sungkyunkwan University, Suwon, Republic of Korea.
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19
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Tang L, Wang XJ, Baba H, Giganti F. Gastric cancer and image-derived quantitative parameters: Part 2-a critical review of DCE-MRI and 18F-FDG PET/CT findings. Eur Radiol 2019; 30:247-260. [PMID: 31392480 PMCID: PMC6890619 DOI: 10.1007/s00330-019-06370-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 05/31/2019] [Accepted: 07/12/2019] [Indexed: 12/13/2022]
Abstract
Abstract There is yet no consensus on the application of functional imaging and qualitative image interpretation in the management of gastric cancer. In this second part, we will discuss the role of image-derived quantitative parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in gastric cancer, as both techniques have been shown to be promising and useful tools in the clinical decision making of this disease. We will focus on different aspects including aggressiveness assessment, staging and Lauren type discrimination, prognosis prediction and response evaluation. Although both the number of articles and the patients enrolled in the studies were rather small, there is evidence that quantitative parameters from DCE-MRI such as Ktrans, Ve, Kep and AUC could be promising image-derived surrogate parameters for the management of gastric cancer. Data from 18F-FDG PET/CT studies showed that standardised uptake value (SUV) is significantly associated with the aggressiveness, treatment response and prognosis of this disease. Along with the results from diffusion-weighted MRI and contrast-enhanced multidetector computed tomography presented in Part 1 of this critical review, there are additional image-derived quantitative parameters from DCE-MRI and 18F-FDG PET/CT that hold promise as effective tools in the diagnostic pathway of gastric cancer. Key Points • Quantitative analysis from DCE-MRI and18F-FDG PET/CT allows the extrapolation of multiple image-derived parameters. • Data from DCE-MRI (Ktrans, Ve, Kep and AUC) and 18F-FDG PET/CT (SUV) are non-invasive, quantitative image-derived parameters that hold promise in the evaluation of the aggressiveness, treatment response and prognosis of gastric cancer.
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Affiliation(s)
- Lei Tang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital, Beijing, China
| | - Xue-Juan Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Nuclear Medicine, Peking University Cancer Hospital, Beijing, China
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK. .,Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, 3rd Floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
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20
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Bartoš M, Rajmic P, Šorel M, Mangová M, Keunen O, Jiřík R. Spatially regularized estimation of the tissue homogeneity model parameters in DCE-MRI using proximal minimization. Magn Reson Med 2019; 82:2257-2272. [PMID: 31317577 DOI: 10.1002/mrm.27874] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 04/24/2019] [Accepted: 05/29/2019] [Indexed: 12/26/2022]
Abstract
PURPOSE The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast-enhanced MRI (DCE-MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasma flow and the permeability-surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of the advanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels. THEORY AND METHODS Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically. RESULTS The proposed algorithm helps to reduce noise in the estimated perfusion-parameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real data show improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit. CONCLUSION The reliability of the DCE-MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization.
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Affiliation(s)
- Michal Bartoš
- The Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czech Republic
| | - Pavel Rajmic
- SPLab, Department of Telecommunications, FEEC, Brno University of Technology, Brno, Czech Republic
| | - Michal Šorel
- The Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czech Republic
| | - Marie Mangová
- SPLab, Department of Telecommunications, FEEC, Brno University of Technology, Brno, Czech Republic
| | - Olivier Keunen
- Norlux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Radovan Jiřík
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
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Kadimesetty VS, Gutta S, Ganapathy S, Yalavarthy PK. Convolutional Neural Network-Based Robust Denoising of Low-Dose Computed Tomography Perfusion Maps. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2018.2860788] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Dynamic contrast-enhanced MRI of malignant pleural mesothelioma: a comparative study of pharmacokinetic models and correlation with mRECIST criteria. Cancer Imaging 2019; 19:10. [PMID: 30813957 PMCID: PMC6391827 DOI: 10.1186/s40644-019-0189-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 01/16/2019] [Indexed: 12/29/2022] Open
Abstract
Background Malignant pleural mesothelioma (MPM) is a rare and aggressive thoracic malignancy that is difficult to cure. Dynamic contrast-enhanced (DCE) MRI is a functional imaging technique used to analyze tumor microvascular properties and to monitor therapy response. Purpose of this study was to compare two tracer kinetic models, the extended Tofts (ET) and the adiabatic approximation tissue homogeneity model (AATH) for analysis of DCE-MRI and examine the value of the DCE parameters to predict response to chemotherapy in patients with MPM. Method This prospective, longitudinal, single tertiary radiology center study was conducted between October 2013 and July 2015. Patient underwent DCE-MRI studies at three time points: prior to therapy, during and after cisplatin-based chemotherapy. The images were analyzed using ET and AATH models. In short-term follow-up, the patients were classified as having disease control or progressive disease according to modified response evaluation criteria in solid tumors (mRECIST) criteria. Receiver operating characteristic curve analysis was used to examine specificity and sensitivity of DCE parameters for predicting response to therapy. Comparison tests were used to analyze whether derived parameters are interchangeable between the two models. Results Nineteen patients form the study population. The results indicate that the derived parameters are not interchangeable between the models. Significant correlation with response to therapy was found for AATH-calculated median pre-treatment efflux rate (kep) showing sensitivity of 83% and specificity of 100% (AUC 0.9). ET-calculated maximal pre-treatment kep showed 100% sensitivity and specificity for predicting treatment response during the early phase of the therapy and reached a favorable trend to significant prognostic value post-therapy. Conclusion Both models show potential in predicting response to therapy in MPM. High pre-treatment kep values suggest MPM disease control post-chemotherapy. Electronic supplementary material The online version of this article (10.1186/s40644-019-0189-5) contains supplementary material, which is available to authorized users.
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Eck BL, Muzic RF, Levi J, Wu H, Fahmi R, Li Y, Fares A, Vembar M, Dhanantwari A, Bezerra HG, Wilson DL. The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT. Phys Med Biol 2018; 63:185011. [PMID: 30113311 PMCID: PMC6264889 DOI: 10.1088/1361-6560/aadab6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In this work, we clarified the role of acquisition parameters and quantification methods in myocardial blood flow (MBF) estimability for myocardial perfusion imaging using CT (MPI-CT). We used a physiologic model with a CT simulator to generate time-attenuation curves across a range of imaging conditions, i.e. tube current-time product, imaging duration, and temporal sampling, and physiologic conditions, i.e. MBF and arterial input function width. We assessed MBF estimability by precision (interquartile range of MBF estimates) and bias (difference between median MBF estimate and reference MBF) for multiple quantification methods. Methods included: six existing model-based deconvolution models, such as the plug-flow tissue uptake model (PTU), Fermi function model, and single-compartment model (SCM); two proposed robust physiologic models (RPM1, RPM2); model-independent singular value decomposition with Tikhonov regularization determined by the L-curve criterion (LSVD); and maximum upslope (MUP). Simulations show that MBF estimability is most affected by changes in imaging duration for model-based methods and by changes in tube current-time product and sampling interval for model-independent methods. Models with three parameters, i.e. RPM1, RPM2, and SCM, gave least biased and most precise MBF estimates. The average relative bias (precision) for RPM1, RPM2, and SCM was ⩽11% (⩽10%) and the models produced high-quality MBF maps in CT simulated phantom data as well as in a porcine model of coronary artery stenosis. In terms of precision, the methods ranked best-to-worst are: RPM1 > RPM2 > Fermi > SCM > LSVD > MUP [Formula: see text] other methods. In terms of bias, the models ranked best-to-worst are: SCM > RPM2 > RPM1 > PTU > LSVD [Formula: see text] other methods. Models with four or more parameters, particularly five-parameter models, had very poor precision (as much as 310% uncertainty) and/or significant bias (as much as 493%) and were sensitive to parameter initialization, thus suggesting the presence of multiple local minima. For improved estimates of MBF from MPI-CT, it is recommended to use reduced models that incorporate prior knowledge of physiology and contrast agent uptake, such as the proposed RPM1 and RPM2 models.
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Affiliation(s)
- Brendan L Eck
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
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Simultaneous multislice acquisition with multi-contrast segmented EPI for separation of signal contributions in dynamic contrast-enhanced imaging. PLoS One 2018; 13:e0202673. [PMID: 30153275 PMCID: PMC6112664 DOI: 10.1371/journal.pone.0202673] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 08/07/2018] [Indexed: 11/27/2022] Open
Abstract
We present a method to efficiently separate signal in magnetic resonance imaging (MRI) into a base signal S0, representing the mainly T1-weighted component without T2*-relaxation, and its T2*-weighted counterpart by the rapid acquisition of multiple contrasts for advanced pharmacokinetic modelling. This is achieved by incorporating simultaneous multislice (SMS) imaging into a multi-contrast, segmented echo planar imaging (EPI) sequence to allow extended spatial coverage, which covers larger body regions without time penalty. Simultaneous acquisition of four slices was combined with segmented EPI for fast imaging with three gradient echo times in a preclinical perfusion study. Six female domestic pigs, German-landrace or hybrid-form, were scanned for 11 minutes respectively during administration of gadolinium-based contrast agent. Influences of reconstruction methods and training data were investigated. The separation into T1- and T2*-dependent signal contributions was achieved by fitting a standard analytical model to the acquired multi-echo data. The application of SMS yielded sufficient temporal resolution for the detection of the arterial input function in major vessels, while anatomical coverage allowed perfusion analysis of muscle tissue. The separation of the MR signal into T1- and T2*-dependent components allowed the correction of susceptibility related changes. We demonstrate a novel sequence for dynamic contrast-enhanced MRI that meets the requirements of temporal resolution (Δt < 1.5 s) and image quality. The incorporation of SMS into multi-contrast, segmented EPI can overcome existing limitations of dynamic contrast enhancement and dynamic susceptibility contrast methods, when applied separately. The new approach allows both techniques to be combined in a single acquisition with a large spatial coverage.
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Niu T, Yang P, Sun X, Mao T, Xu L, Yue N, Kuang Y, Shi L, Nie K. Variations of quantitative perfusion measurement on dynamic contrast enhanced CT for colorectal cancer: implication of standardized image protocol. Phys Med Biol 2018; 63:165009. [PMID: 29889046 DOI: 10.1088/1361-6560/aacb99] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Tumor angiogenesis is considered an important prognostic factor. With an increasing emphasis on imaging evaluation of the tumor microenvironment, dynamic contrast enhanced-computed tomography (DCE-CT) has evolved as an important functional technique in this setting. Yet many questions remain as to how and when these functional measurements should be performed for each agent and tumor type, and what quantitative models should be used in the fitting process. In this study, we evaluated the variations of perfusion measurement on DCE-CT for rectal cancer patients from (1) different tracer kinetic models, (2) different scan acquisition lengths, and (3) different scan intervals. A total of seven commonly used models were studied: the adiabatic approximation to the tissue homogeneity (AATH) model, adiabatic approximation to the homogeneity tissue with fixed transit time (AATHFT) model, the Tofts model (TM), the extended Tofts model (ETM), Patlak model, Logan model, and the model-free deconvolution method. Akaike's information criterion was used to identify the best fitting model. The interchangeability of different models was further evaluated using Bland-Altman analysis. All models gave comparable blood volume (BV) measurements except the Patlak method. While for the volume transfer constant (Ktrans) estimation, AATHFT, AATH, and ETM generated reasonable agreement among each other but not for the other models. Regarding the blood flow (BF) measurement, no two models were interchangeable. In addition, the perfusion parameters were compared with four acquisition times (45, 65, 85, and 105 s) and four temporal intervals (1, 2, 3, and 4 s). No significant difference was observed in the volume transfer constant (Ktrans), BV, and BF measurements when comparing data acquired over 65 s with data acquired over 105 s using any of the DCE models in this study. Yet increasing the temporal interval led to a significant overestimation of BF in the deconvolution method. In conclusion, the perfusion measurement is indeed model dependent and the image acquisition/processing technique is dependent. The radiation dose of DCE-CT was an average of 1.5-2 times an abdomen/pelvic CT, which is not insubstantial. To take the DCE-CT forward as a biomarker in oncology, prospective studies should be carefully designed with the optimal image acquisition and analysis technique.
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Affiliation(s)
- Tianye Niu
- Institute of Translational Medicine, Zhejiang University, Hangzhou 310013, People's Republic of China. Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310019, People's Republic of China. Both authors contribute equally
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Hupple CW, Morscher S, Burton NC, Pagel MD, McNally LR, Cárdenas-Rodríguez J. A light-fluence-independent method for the quantitative analysis of dynamic contrast-enhanced multispectral optoacoustic tomography (DCE MSOT). PHOTOACOUSTICS 2018; 10:54-64. [PMID: 29988890 PMCID: PMC6033053 DOI: 10.1016/j.pacs.2018.04.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 04/24/2018] [Accepted: 04/26/2018] [Indexed: 05/20/2023]
Abstract
MultiSpectral Optoacoustic Tomography (MSOT) is an emerging imaging technology that allows for data acquisition at high spatial and temporal resolution. These imaging characteristics are advantageous for Dynamic Contrast Enhanced (DCE) imaging that can assess the combination of vascular flow and permeability. However, the quantitative analysis of DCE MSOT data has not been possible due to complications caused by wavelength-dependent light attenuation and variability in light fluence at different anatomical locations. In this work we present a new method for the quantitative analysis of DCE MSOT data that is not biased by light fluence. We have named this method the two-compartment linear standard model (2C-LSM) for DCE MSOT.
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Affiliation(s)
| | | | | | - Mark D. Pagel
- Department of Medical Imaging, University of Arizona, Tucson, AZ, USA
| | - Lacey R. McNally
- Department of Medicine, University of Louisville, Louisville, KY, USA
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A simulation study comparing nine mathematical models of arterial input function for dynamic contrast enhanced MRI to the Parker model. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:507-518. [DOI: 10.1007/s13246-018-0632-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 03/20/2018] [Indexed: 02/06/2023]
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Pineda FD, Easley TO, Karczmar GS. Dynamic field-of-view imaging to increase temporal resolution in the early phase of contrast media uptake in breast DCE-MRI: A feasibility study. Med Phys 2018; 45:1050-1058. [PMID: 29314060 PMCID: PMC6028013 DOI: 10.1002/mp.12747] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 12/14/2017] [Accepted: 12/15/2017] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To increase diagnostic accuracy of breast MRI by increasing temporal resolution and more accurately sampling the early kinetics of contrast media uptake. We tested the feasibility of accelerating bilateral breast DCE-MRI by reducing the FOV, allowing aliasing, and unfolding the resulting images. METHODS Previous experience with an "ultrafast" protocol for bilateral breast DCE-MRI (6-10 s temporal resolution) showed that the number of significantly enhancing voxels is very low in the first 30-45 s after contrast media injection. This suggests that overlap of enhancing voxels in aliased images will be very infrequent. Therefore, aliased images can be acquired during the first 30-45 s after contrast media injection and unfolded to produce full-FOV images with few errors. In a proof-of-principle test, aliased images were simulated from the first 30 s of full-FOV acquisitions. Cases with relatively dense early enhancement were selected to test this method in a worst-case scenario. In an initial test, an FOV of 60% the size of the full FOV was simulated. To reduce the probability of errors due to overlapping voxels in aliased images, we then tested a dynamic FOV approach. The FOV was progressively increased so that enhancing voxels could not overlap at multiple time-points, and areas where enhancing voxels overlapped at a given time-point could be unfolded by interpolating between the preceding and subsequent time-points (acquired with different FOVs). The simulated FOV sizes for each of the time-points were 31%, 44%, and 77% of the full FOV. Subtraction images (post- minus precontrast) were generated for aliased images and filtered to select significantly enhancing voxels. Comparison of early, highly aliased images, with later, less aliased images then helped to identify the true locations of enhancing voxels. RESULTS In the initial aliasing simulations, an average of 2.9% of the enhancing voxels above the chest wall overlapped in the aliased images (range 0.1%-6.7%). The similarity between simulated unfolded images and the correct full-FOV images, evaluated using CW-SSIM (complex wavelet similarity index), was 0.50 ± 0.26, 0.76 ± 0.09, and 0.80 ± 0.10 for the first, second, and third time-point, respectively (numbers closer to 1 indicate more similar images). For the dynamic FOV tests, an average of 11% of the enhancing voxels above the chest wall overlapped (range 0%-40%) due to greater aliasing at early time-points. Despite more voxels overlapping, the CW-SSIM values for the data acquired with dynamic FOVs were 0.64 ± 0.25, 0.93 ± 0.04, and 0.97 ± 0.02 for the first, second, and third time-points, respectively. CONCLUSIONS Dynamic FOV imaging allows accelerated bilateral breast DCE-MRI during the early contrast media uptake phase. This method relies on the sparsity of enhancement at the early phases of DCE-MRI of the breast. The results of simulations suggest that dynamic FOV imaging and unfolding produces images that are very close to fully sampled images, and allows temporal resolution as high as 2 s per image.
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Affiliation(s)
| | - Ty O Easley
- Department of RadiologyThe University of ChicagoChicagoIL60637USA
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29
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Li HH, Zhu H, Yue L, Fu Y, Grimm R, Stemmer A, Fu CX, Peng WJ. Feasibility of free-breathing dynamic contrast-enhanced MRI of gastric cancer using a golden-angle radial stack-of-stars VIBE sequence: comparison with the conventional contrast-enhanced breath-hold 3D VIBE sequence. Eur Radiol 2017; 28:1891-1899. [PMID: 29260366 DOI: 10.1007/s00330-017-5193-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 11/07/2017] [Accepted: 11/13/2017] [Indexed: 01/23/2023]
Abstract
OBJECTIVES To investigate the feasibility and diagnostic value of free-breathing, radial, stack-of-stars three-dimensional (3D) gradient echo (GRE) sequence ("golden angle") on dynamic contrast-enhanced (DCE) MRI of gastric cancer. METHODS Forty-three gastric cancer patients were divided into cooperative and uncooperative groups. Respiratory fluctuation was observed using an abdominal respiratory gating sensor. Those who breath-held for more than 15 s were placed in the cooperative group and the remainder in the uncooperative group. The 3-T MRI scanning protocol included 3D GRE and conventional breath-hold VIBE (volume-interpolated breath-hold examination) sequences, comparing images quantitatively and qualitatively. DCE-MRI parameters from VIBE images of normal gastric wall and malignant lesions were compared. RESULTS For uncooperative patients, 3D GRE scored higher qualitatively, and had higher SNRs (signal-to-noise ratios) and CNRs (contrast-to-noise ratios) than conventional VIBE quantitatively. Though 3D GRE images scored lower in qualitative parameters compared with conventional VIBE for cooperative patients, it provided images with fewer artefacts. DCE parameters differed significantly between normal gastric wall and lesions, with higher Ve (extracellular volume) and lower Kep (reflux constant) in gastric cancer. CONCLUSIONS The free-breathing, golden-angle, radial stack-of-stars 3D GRE technique is feasible for DCE-MRI of gastric cancer. Dynamic enhanced images can be used for quantitative analysis of this malignancy. KEY POINTS • Golden-angle radial stack-of-stars VIBE aids gastric cancer MRI diagnosis. • The 3D GRE technique is suitable for patients unable to suspend respiration. • Method scored higher in the qualitative evaluation for uncooperative patients. • The technique produced images with fewer artefacts than conventional VIBE sequence. • Dynamic enhanced images can be used for quantitative analysis of gastric cancer.
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Affiliation(s)
- Huan-Huan Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Radiology, Shanghai Pudong New Area Gongli Hospital, Shanghai, China
| | - Hui Zhu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Lei Yue
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yi Fu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Robert Grimm
- MR Applications Development, Siemens Healthcare, Erlangen, Germany
| | - Alto Stemmer
- MR Applications Development, Siemens Healthcare, Erlangen, Germany
| | - Cai-Xia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Wei-Jun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
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van de Haar HJ, Jansen JFA, Jeukens CRLPN, Burgmans S, van Buchem MA, Muller M, Hofman PAM, Verhey FRJ, van Osch MJP, Backes WH. Subtle blood-brain barrier leakage rate and spatial extent: Considerations for dynamic contrast-enhanced MRI. Med Phys 2017; 44:4112-4125. [PMID: 28493613 DOI: 10.1002/mp.12328] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 03/29/2017] [Accepted: 04/17/2017] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Dynamic contrast-enhanced (DCE) MRI can be used to measure blood-brain barrier (BBB) leakage. In neurodegenerative disorders such as small vessel disease and dementia, the leakage can be very subtle and the corresponding signal can be rather noisy. For these reasons, an optimized DCE-MRI measurement and study design is required. To this end, a new measure indicative of the spatial extent of leakage is introduced and the effects of scan time and sample size are explored. METHODS Dual-time resolution DCE-MRI was performed in 16 patients with early Alzheimer's disease (AD) and 17 healthy controls. The leakage rate (Ki ) and volume fraction of detectable leaking tissue (vL ) to quantify the spatial extent of BBB leakage were calculated in cortical gray matter and white matter using noise-corrected histogram analysis of leakage maps. Computer simulations utilizing realistic Ki histograms, mimicking the strong effect of noise and variation in Ki values, were performed to understand the influence of scan time on the estimated leakage. RESULTS The mean Ki was very low (order of 10-4 min-1 ) and highly influenced by noise, causing the Ki to be increasingly overestimated at shorter scan times. In the white matter, the Ki was not different between patients with early AD and controls, but was higher in the cortex for patients, reaching significance after 14.5 min of scan time. To detect group differences, vL proved more suitable, showing significantly higher values for patients compared with controls in the cortex after 8 minutes of scan time, and in white matter after 15.5 min. CONCLUSIONS Several ways to improve the sensitivity of a DCE-MRI experiment to subtle BBB leakage were presented. We have provided vL as an attractive and potentially more time-efficient alternative to detect group differences in subtle and widespread blood-brain barrier leakage compared with leakage rate Ki . Recommendations on group size and scan time are made based on statistical power calculations to aid future research.
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Affiliation(s)
- Harm J van de Haar
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, PO box 5800, Maastricht, 6202 AZ, The Netherlands.,Department of Neuropsychology and Psychiatry/Alzheimer Center Limburg, Maastricht University Medical Center, PO box 616, Maastricht, 6200 MD, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, PO box 616, Maastricht, 6200 MD, The Netherlands
| | - Jacobus F A Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, PO box 5800, Maastricht, 6202 AZ, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, PO box 616, Maastricht, 6200 MD, The Netherlands
| | - Cécile R L P N Jeukens
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, PO box 5800, Maastricht, 6202 AZ, The Netherlands
| | - Saartje Burgmans
- Department of Neuropsychology and Psychiatry/Alzheimer Center Limburg, Maastricht University Medical Center, PO box 616, Maastricht, 6200 MD, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, PO box 616, Maastricht, 6200 MD, The Netherlands
| | - Mark A van Buchem
- Department of Radiology, Leiden University Medical Center, PO box 9600, Leiden, 2300 RC, The Netherlands
| | - Majon Muller
- Department of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, Leiden, 2300 RC, The Netherlands
| | - Paul A M Hofman
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, PO box 5800, Maastricht, 6202 AZ, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, PO box 616, Maastricht, 6200 MD, The Netherlands
| | - Frans R J Verhey
- Department of Neuropsychology and Psychiatry/Alzheimer Center Limburg, Maastricht University Medical Center, PO box 616, Maastricht, 6200 MD, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, PO box 616, Maastricht, 6200 MD, The Netherlands
| | - Matthias J P van Osch
- Department of Radiology, Leiden University Medical Center, PO box 9600, Leiden, 2300 RC, The Netherlands
| | - Walter H Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, PO box 5800, Maastricht, 6202 AZ, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, PO box 616, Maastricht, 6200 MD, The Netherlands
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Li CH, Chen FH, Schellingerhout D, Lin YS, Hong JH, Liu HL. Flow versus permeability weighting in estimating the forward volumetric transfer constant (K trans) obtained by DCE-MRI with contrast agents of differing molecular sizes. Magn Reson Imaging 2016; 36:105-111. [PMID: 27989901 DOI: 10.1016/j.mri.2016.10.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 10/26/2016] [Indexed: 01/02/2023]
Abstract
PURPOSE To quantify the differential plasma flow- (Fp-) and permeability surface area product per unit mass of tissue- (PS-) weighting in forward volumetric transfer constant (Ktrans) estimates by using a low molecular (Gd-DTPA) versus high molecular (Gadomer) weight contrast agent in dynamic contrast enhanced (DCE) MRI. MATERIALS AND METHODS DCE MRI was performed using a 7T animal scanner in 14 C57BL/6J mice syngeneic for TRAMP tumors, by administering Gd-DTPA (0.9kD) in eight mice and Gadomer (35kD) in the remainder. The acquisition time was 10min with a sampling rate of one image every 2s. Pharmacokinetic modeling was performed to obtain Ktrans by using Extended Tofts model (ETM). In addition, the adiabatic approximation to the tissue homogeneity (AATH) model was employed to obtain the relative contributions of Fp and PS. RESULTS The Ktrans values derived from DCE-MRI with Gd-DTPA showed significant correlations with both PS (r2=0.64, p=0.009) and Fp (r2=0.57, p=0.016), whereas those with Gadomer were found only significantly correlated with PS (r2=0.96, p=0.0003) but not with Fp (r2=0.34, p=0.111). A voxel-based analysis showed that Ktrans approximated PS (<30% difference) in 78.3% of perfused tumor volume for Gadomer, but only 37.3% for Gd-DTPA. CONCLUSIONS The differential contributions of Fp and PS in estimating Ktrans values vary with the molecular weight of the contrast agent used. The macromolecular contrast agent resulted in Ktrans values that were much less dependent on flow. These findings support the use of macromolecular contrast agents for estimating tumor vessel permeability with DCE-MRI.
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Affiliation(s)
- Cheng-He Li
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Fang-Hsin Chen
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Radiation Biology Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Radiation Oncology, Chang Gung Memorial Hospital at Linko, Taoyuan, Taiwan
| | - Dawid Schellingerhout
- Departments of Diagnostic Radiology and Cancer Systems Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Yu-Shi Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ji-Hong Hong
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Radiation Biology Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Radiation Oncology, Chang Gung Memorial Hospital at Linko, Taoyuan, Taiwan
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
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Quantitative Perfusion Analysis of First-Pass Contrast Enhancement Kinetics: Application to MRI of Myocardial Perfusion in Coronary Artery Disease. PLoS One 2016; 11:e0162067. [PMID: 27583385 PMCID: PMC5008793 DOI: 10.1371/journal.pone.0162067] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Accepted: 08/17/2016] [Indexed: 11/22/2022] Open
Abstract
Purpose Perfusion analysis from first-pass contrast enhancement kinetics requires modeling tissue contrast exchange. This study presents a new approach for numerical implementation of the tissue homogeneity model, incorporating flexible distance steps along the capillary (NTHf). Methods The proposed NTHf model considers contrast exchange in fluid packets flowing along the capillary, incorporating flexible distance steps, thus allowing more efficient and stable calculations of the transit of tracer through the tissue. We prospectively studied 8 patients (62 ± 13 years old) with suspected CAD, who underwent first-pass perfusion CMR imaging at rest and stress prior to angiography. Myocardial blood flow (MBF) and myocardial perfusion reserve index (MPRI) were estimated using both the NTHf and the conventional adiabatic approximation of the TH models. Coronary artery lesions detected at angiography were clinically assigned to one of three categories of stenosis severity (‘insignificant’, ‘mild to moderate’ and ‘severe’) and related to corresponding myocardial territories. Results The mean MBF (ml/g/min) at rest/stress and MPRI were 0.80 ± 0.33/1.25 ± 0.45 and 1.68 ± 0.54 in the insignificant regions, 0.74 ± 0.21/1.09 ± 0.28 and 1.54 ± 0.46 in the mild to moderate regions, and 0.79 ± 0.28/0.63 ± 0.34 and 0.85 ± 0.48 in the severe regions, respectively. The correlation coefficients of MBFs at rest/stress and MPRI between the NTHf and AATH models were r = 0.97/0.93 and r = 0.91, respectively. Conclusions The proposed NTHf model allows efficient quantitative analysis of the transit of tracer through tissue, particularly at higher flow. Results of initial application to MRI of myocardial perfusion in CAD are encouraging.
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Turco S, Wijkstra H, Mischi M. Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review. IEEE Rev Biomed Eng 2016; 9:121-47. [PMID: 27337725 DOI: 10.1109/rbme.2016.2583541] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.
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Quantitative Myocardial Perfusion with Dynamic Contrast-Enhanced Imaging in MRI and CT: Theoretical Models and Current Implementation. BIOMED RESEARCH INTERNATIONAL 2016; 2016:1734190. [PMID: 27088083 PMCID: PMC4806267 DOI: 10.1155/2016/1734190] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 02/11/2016] [Indexed: 01/21/2023]
Abstract
Technological advances in magnetic resonance imaging (MRI) and computed tomography (CT), including higher spatial and temporal resolution, have made the prospect of performing absolute myocardial perfusion quantification possible, previously only achievable with positron emission tomography (PET). This could facilitate integration of myocardial perfusion biomarkers into the current workup for coronary artery disease (CAD), as MRI and CT systems are more widely available than PET scanners. Cardiac PET scanning remains expensive and is restricted by the requirement of a nearby cyclotron. Clinical evidence is needed to demonstrate that MRI and CT have similar accuracy for myocardial perfusion quantification as PET. However, lack of standardization of acquisition protocols and tracer kinetic model selection complicates comparison between different studies and modalities. The aim of this overview is to provide insight into the different tracer kinetic models for quantitative myocardial perfusion analysis and to address typical implementation issues in MRI and CT. We compare different models based on their theoretical derivations and present the respective consequences for MRI and CT acquisition parameters, highlighting the interplay between tracer kinetic modeling and acquisition settings.
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Optimized Fast Dynamic Contrast-Enhanced Magnetic Resonance Imaging of the Prostate. Invest Radiol 2016; 51:106-12. [DOI: 10.1097/rli.0000000000000213] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lebel RM, Jones J, Ferre JC, Law M, Nayak KS. Highly accelerated dynamic contrast enhanced imaging. Magn Reson Med 2016; 71:635-44. [PMID: 23504992 DOI: 10.1002/mrm.24710] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
PURPOSE Dynamic contrast-enhanced imaging provides unique physiological information, notably the endothelial permeability (K(trans)), and may improve the diagnosis and management of multiple pathologies. Current acquisition methods provide limited spatial-temporal resolution and field-of-view, often preventing characterization of the entire pathology and precluding measurement of the arterial input function. We present a method for highly accelerated dynamic imaging and demonstrate its utility for dynamic contrast-enhanced modeling. METHODS We propose a novel Poisson ellipsoid sampling scheme and enforce multiple spatial and temporal l1-norm constraints during image reconstruction. Retrospective and prospective analyses were performed to validate the approach. RESULTS Retrospectively, no mean bias or diverging trend was observed as the acceleration rate was increased from 3× to 18×; less than 10% error was measured in K(trans) at any individual rates in this range. Prospectively accelerated images at a rate of 36× enabled full brain coverage with 0.94 × 0.94 × 1.9 mm(3) spatial and 4.1 s temporal resolutions. Images showed no visible degradation and provided accurate K(trans) values when compared to a clinical population. CONCLUSION Highly accelerated dynamic MRI using compressed sensing and parallel imaging provides accurate permeability modeling and enables full brain, high resolution acquisitions.
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Affiliation(s)
- Robert Marc Lebel
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
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Feasibility of CAIPIRINHA-Dixon-TWIST-VIBE for dynamic contrast-enhanced MRI of the prostate. Eur J Radiol 2015; 84:2110-6. [DOI: 10.1016/j.ejrad.2015.08.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Accepted: 08/19/2015] [Indexed: 11/22/2022]
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Texture analysis on MR images helps predicting non-response to NAC in breast cancer. BMC Cancer 2015; 15:574. [PMID: 26243303 PMCID: PMC4526309 DOI: 10.1186/s12885-015-1563-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 07/16/2015] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI. METHODS Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC. RESULTS A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction. CONCLUSION Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.
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Barnes SR, Ng TSC, Montagne A, Law M, Zlokovic BV, Jacobs RE. Optimal acquisition and modeling parameters for accurate assessment of low Ktrans blood-brain barrier permeability using dynamic contrast-enhanced MRI. Magn Reson Med 2015; 75:1967-77. [PMID: 26077645 DOI: 10.1002/mrm.25793] [Citation(s) in RCA: 94] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Revised: 04/30/2015] [Accepted: 04/30/2015] [Indexed: 01/09/2023]
Abstract
PURPOSE To determine optimal parameters for acquisition and processing of dynamic contrast-enhanced MRI (DCE-MRI) to detect small changes in near normal low blood-brain barrier (BBB) permeability. METHODS Using a contrast-to-noise ratio metric (K-CNR) for Ktrans precision and accuracy, the effects of kinetic model selection, scan duration, temporal resolution, signal drift, and length of baseline on the estimation of low permeability values was evaluated with simulations. RESULTS The Patlak model was shown to give the highest K-CNR at low Ktrans . The Ktrans transition point, above which other models yielded superior results, was highly dependent on scan duration and tissue extravascular extracellular volume fraction (ve ). The highest K-CNR for low Ktrans was obtained when Patlak model analysis was combined with long scan times (10-30 min), modest temporal resolution (<60 s/image), and long baseline scans (1-4 min). Signal drift as low as 3% was shown to affect the accuracy of Ktrans estimation with Patlak analysis. CONCLUSION DCE acquisition and modeling parameters are interdependent and should be optimized together for the tissue being imaged. Appropriately optimized protocols can detect even the subtlest changes in BBB integrity and may be used to probe the earliest changes in neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis.
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Affiliation(s)
- Samuel R Barnes
- Beckman Institute, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Thomas S C Ng
- Beckman Institute, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.,Department of Medicine, University of California, Irvine Medical Center, Orange, California, USA
| | - Axel Montagne
- Zilkha Neurogenetic Institute and Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Meng Law
- Division of Neuroradiology, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Berislav V Zlokovic
- Zilkha Neurogenetic Institute and Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Russell E Jacobs
- Beckman Institute, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
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Validation of Perfusion Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using a Blood Pool Contrast Agent in Skeletal Swine Muscle. PLoS One 2015; 10:e0128060. [PMID: 26061498 PMCID: PMC4465215 DOI: 10.1371/journal.pone.0128060] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2015] [Accepted: 04/23/2015] [Indexed: 01/10/2023] Open
Abstract
The purpose of our study was to validate perfusion quantification in a low-perfused tissue by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with shared k-space sampling using a blood pool contrast agent. Perfusion measurements were performed in a total of seven female pigs. An ultrasonic Doppler probe was attached to the right femoral artery to determine total flow in the hind leg musculature. The femoral artery was catheterized for continuous local administration of adenosine to increase blood flow up to four times the baseline level. Three different stable perfusion levels were induced. The MR protocol included a 3D gradient-echo sequence with a temporal resolution of approximately 1.5 seconds. Before each dynamic sequence, static MR images were acquired with flip angles of 5°, 10°, 20°, and 30°. Both static and dynamic images were used to generate relaxation rate and baseline magnetization maps with a flip angle method. 0.1 mL/kg body weight of blood pool contrast medium was injected via a central venous catheter at a flow rate of 5 mL/s. The right hind leg was segmented in 3D into medial, cranial, lateral, and pelvic thigh muscles, lower leg, bones, skin, and fat. The arterial input function (AIF) was measured in the aorta. Perfusion of the different anatomic regions was calculated using a one- and a two-compartment model with delay- and dispersion-corrected AIFs. The F-test for model comparison was used to decide whether to use the results of the one- or two-compartment model fit. Total flow was calculated by integrating volume-weighted perfusion values over the whole measured region. The resulting values of delay, dispersion, blood volume, mean transit time, and flow were all in physiologically and physically reasonable ranges. In 107 of 160 ROIs, the blood signal was separated, using a two-compartment model, into a capillary and an arteriolar signal contribution, decided by the F-test. Overall flow in hind leg muscles, as measured by the ultrasound probe, highly correlated with total flow determined by MRI, R = 0.89 and P = 10−7. Linear regression yielded a slope of 1.2 and a y-axis intercept of 259 mL/min. The mean total volume of the investigated muscle tissue corresponds to an offset perfusion of 4.7mL/(min ⋅ 100cm3). The DCE-MRI technique presented here uses a blood pool contrast medium in combination with a two-compartment tracer kinetic model and allows absolute quantification of low-perfused non-cerebral organs such as muscles.
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Liu HL, Chang TT, Yan FX, Li CH, Lin YS, Wong AM. Assessment of vessel permeability by combining dynamic contrast-enhanced and arterial spin labeling MRI. NMR IN BIOMEDICINE 2015; 28:642-649. [PMID: 25880892 DOI: 10.1002/nbm.3297] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 02/19/2015] [Accepted: 03/05/2015] [Indexed: 06/04/2023]
Abstract
The forward volumetric transfer constant (K(trans)), a physiological parameter extracted from dynamic contrast-enhanced (DCE) MRI, is weighted by vessel permeability and tissue blood flow. The permeability × surface area product per unit mass of tissue (PS) in brain tumors was estimated in this study by combining the blood flow obtained through pseudo-continuous arterial spin labeling (PCASL) and K(trans) obtained through DCE MRI. An analytical analysis and a numerical simulation were conducted to understand how errors in the flow and K(trans) estimates would propagate to the resulting PS. Fourteen pediatric patients with brain tumors were scanned on a clinical 3-T MRI scanner. PCASL perfusion imaging was performed using a three-dimensional (3D) fast-spin-echo readout module to determine blood flow. DCE imaging was performed using a 3D spoiled gradient-echo sequence, and the K(trans) map was obtained with the extended Tofts model. The numerical analysis demonstrated that the uncertainty of PS was predominantly dependent on that of K(trans) and was relatively insensitive to the flow. The average PS values of the whole tumors ranged from 0.006 to 0.217 min(-1), with a mean of 0.050 min(-1) among the patients. The mean K(trans) value was 18% lower than the PS value, with a maximum discrepancy of 25%. When the parametric maps were compared on a voxel-by-voxel basis, the discrepancies between PS and K(trans) appeared to be heterogeneous within the tumors. The PS values could be more than two-fold higher than the K(trans) values for voxels with high K(trans) levels. This study proposes a method that is easy to implement in clinical practice and has the potential to improve the quantification of the microvascular properties of brain tumors.
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Affiliation(s)
- Ho-Ling Liu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Imaging Physics, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Ting-Ting Chang
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Feng-Xian Yan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Radiology, Taipei Medical University/Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Cheng-He Li
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yu-Shi Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Alex M Wong
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Keelong, Linkou Medical Center, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
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Rukat T, Walker-Samuel S, Reinsberg SA. Dynamic contrast-enhanced MRI in mice: an investigation of model parameter uncertainties. Magn Reson Med 2015; 73:1979-87. [PMID: 25052296 DOI: 10.1002/mrm.25319] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Revised: 05/13/2014] [Accepted: 05/23/2014] [Indexed: 11/08/2022]
Abstract
PURPOSE To establish the experimental factors that dominate the uncertainty of hemodynamic parameters in commonly used pharmacokinetic models. METHODS By fitting simulation results from a multiregion tissue exchange model (Multiple path, Multiple tracer, Indicator Dilution, 4 region), the precision and accuracy of hemodynamic parameters in dynamic contrast-enhanced MRI with four tracer kinetic models is investigated. The impact of various injection rates as well as imprecise knowledge of the arterial input functions is examined. RESULTS Fast injections are beneficial for K(trans) precision within the extended Tofts model and within the two-compartment exchange model but do not affect the other models under investigation. Biases from errors in the arterial input functions are mostly consistent in size and direction for the simple and the extended Tofts model, while they are hardly predictable for the other models. Errors in the hematocrit introduce the greatest loss in parameter accuracy, amounting to an average K(trans) bias of 40% for a 30% overestimation throughout all models. CONCLUSION This simulation study allows the detailed inspection of the isolated impact from various experimental conditions on parameter uncertainty. Because parameter uncertainty comparable to human studies was found, this study represents a validation of preclinical dynamic contrast-enhanced MRI for modeling human tumor physiology.
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Affiliation(s)
- Tammo Rukat
- Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada; Department of Physics, Humboldt University, Berlin, Germany
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Oosterbroek J, Bennink E, Philippens MEP, Raaijmakers CPJ, Viergever MA, de Jong HWAM. Comparison of DCE-CT models for quantitative evaluation ofKtransin larynx tumors. Phys Med Biol 2015; 60:3759-73. [DOI: 10.1088/0031-9155/60/9/3759] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Anti-angiogenic Effects of Bumetanide Revealed by DCE-MRI with a Biodegradable Macromolecular Contrast Agent in a Colon Cancer Model. Pharm Res 2015; 32:3029-43. [PMID: 25840948 DOI: 10.1007/s11095-015-1684-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 03/19/2015] [Indexed: 10/23/2022]
Abstract
PURPOSE To assess the antiangiogenic effect of bumetanide with dynamic contrast enhanced (DCE)-MRI and a biodegradable macromolecular MRI contrast agent. METHODS A new polydisulfide containing macrocyclic gadolinium (Gd(III)) chelates, poly([(Gd-DOTA)-DETA]-co-DTBP) (GODP), was synthesized as a safe biodegradable macromolecular MRI contrast agent for DCE-MRI. Nude mice bearing flank HT29 colon cancer xenografts were then treated daily with either bumetanide or saline for a total of 3 weeks. DCE-MRI was performed before and after the treatment weekly. The DCE-MRI data were analyzed using the adiabiatic approximation to the tissue homogeneity (AATH) model to assess the change of tumor vascularity in response to the treatment. Immunohistochemistry (IHC) and western blot were performed to study tumor angiogenic biomarkers and hypoxia. RESULTS DCE-MRI with GODP revealed that bumetanide reduced vascular permeability and plasma volume fraction by a significantly greater extent than the saline control therapy after 3 weeks of therapy. These changes were verified by the significant decline of CD31 and VEGF expression in the bumetanide treatment group. Despite a significant regression in vascularity, the tumors remained highly proliferative. Overexpression of the transcription factor HIF-1α in response to elevated hypoxia is thought to be the driving force behind the uninterrupted tumor expansion. CONCLUSION This study demonstrated the effectiveness of DCE-MRI with GODP in detecting vascular changes following the administration of bumetanide. Bumetanide has the potential to curtail growth of the tumor vasculature and can be employed in future therapeutic strategies.
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Jacobs I, Strijkers GJ, Keizer HM, Janssen HM, Nicolay K, Schabel MC. A novel approach to tracer-kinetic modeling for (macromolecular) dynamic contrast-enhanced MRI. Magn Reson Med 2015; 75:1142-53. [PMID: 25846802 DOI: 10.1002/mrm.25704] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 02/26/2015] [Accepted: 02/26/2015] [Indexed: 12/21/2022]
Abstract
PURPOSE To develop a novel tracer-kinetic modeling approach for multi-agent dynamic contrast-enhanced MRI (DCE-MRI) that facilitates separate estimation of parameters characterizing blood flow and microvascular permeability within one individual. METHODS Monte Carlo simulations were performed to investigate the performance of the constrained multi-agent model. Subsequently, multi-agent DCE-MRI was performed on tumor-bearing mice (n = 5) on a 7T Bruker scanner on three measurement days, in which two dendrimer-based contrast agents having high and intermediate molecular weight, respectively, along with gadoterate meglumine, were sequentially injected within one imaging session. Multi-agent data were simultaneously fit with the gamma capillary transit time model. Blood flow, mean capillary transit time, and bolus arrival time were constrained to be identical between the boluses, while extraction fractions and washout rate constants were separately determined for each agent. RESULTS Simulations showed that constrained multi-agent model regressions led to less uncertainty and bias in estimated tracer-kinetic parameters compared with single-bolus modeling. The approach was successfully applied in vivo, and significant differences in the extraction fraction and washout rate constant between the agents, dependent on their molecular weight, were consistently observed. CONCLUSION A novel multi-agent tracer-kinetic modeling approach that enforces self-consistency of model parameters and can robustly characterize tumor vascular status was demonstrated.
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Affiliation(s)
- Igor Jacobs
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Gustav J Strijkers
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | | | | | - Klaas Nicolay
- Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Matthias C Schabel
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, Utah, USA
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Wang C, Yin FF, Chang Z. An efficient calculation method for pharmacokinetic parameters in brain permeability study using dynamic contrast-enhanced MRI. Magn Reson Med 2015; 75:739-49. [PMID: 25820381 DOI: 10.1002/mrm.25659] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Revised: 01/07/2015] [Accepted: 01/28/2015] [Indexed: 01/04/2023]
Abstract
PURPOSE To develop an efficient method for calculating pharmacokinetic (PK) parameters in brain DCE-MRI permeability studies. METHODS A linear least-squares fitting algorithm based on a derivative expression of the two-compartment PK model was proposed to analytically solve for the PK parameters. Noise in the expression was minimized through low-pass filtering. Simulation studies were conducted in which the proposed method was compared with two existing methods in terms of accuracy and efficiency. Five in vivo brain studies were demonstrated for potential clinical application. RESULTS In the simulation studies using chosen parameter values, the calculated percent difference of K(trans) by the proposed method was <5.0% with a temporal resolution (Δt) < 5 s, and the accuracies of all parameter results were better or comparable to existing methods. When analyzed within certain parameter intensity ranges, the proposed method was more accurate than the existing methods and improved the efficiency by a factor of up to 458 for a Δt = 1 s and up to 38 for a Δt = 5 s. In the in vivo study, the calculated parameters using the proposed method were comparable to those using the existing methods with improved efficiencies. CONCLUSIONS An efficient method was developed for the accurate and efficient calculation of parameters in brain DCE-MRI permeability studies.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
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Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel'farb G, Ouseph R, Dwyer AC. Models and methods for analyzing DCE-MRI: a review. Med Phys 2014; 41:124301. [PMID: 25471985 DOI: 10.1118/1.4898202] [Citation(s) in RCA: 211] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Revised: 09/11/2014] [Accepted: 10/01/2014] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. METHODS DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. RESULTS Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. CONCLUSIONS Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.
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Affiliation(s)
- Fahmi Khalifa
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292 and Electronics and Communication Engineering Department, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Tarek El-Diasty
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Georgy Gimel'farb
- Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
| | - Rosemary Ouseph
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
| | - Amy C Dwyer
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
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Chassidim Y, Vazana U, Prager O, Veksler R, Bar-Klein G, Schoknecht K, Fassler M, Lublinsky S, Shelef I. Analyzing the blood-brain barrier: the benefits of medical imaging in research and clinical practice. Semin Cell Dev Biol 2014; 38:43-52. [PMID: 25455024 DOI: 10.1016/j.semcdb.2014.11.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Revised: 11/23/2014] [Accepted: 11/24/2014] [Indexed: 01/03/2023]
Abstract
A dysfunctional BBB is a common feature in a variety of brain disorders, a fact stressing the need for diagnostic tools designed to assess brain vessels' permeability in space and time. Biological research has benefited over the years various means to analyze BBB integrity. The use of biomarkers for improper BBB functionality is abundant. Systemic administration of BBB impermeable tracers can both visualize brain regions characterized by BBB impairment, as well as lead to its quantification. Additionally, locating molecular, physiological content in regions from which it is restricted under normal BBB functionality undoubtedly indicates brain pathology-related BBB disruption. However, in-depth research into the BBB's phenotype demands higher analytical complexity than functional vs. pathological BBB; criteria which biomarker based BBB permeability analyses do not meet. The involvement of accurate and engineering sciences in recent brain research, has led to improvements in the field, in the form of more accurate, sensitive imaging-based methods. Improvements in the spatiotemporal resolution of many imaging modalities and in image processing techniques, make up for the inadequacies of biomarker based analyses. In pre-clinical research, imaging approaches involving invasive procedures, enable microscopic evaluation of BBB integrity, and benefit high levels of sensitivity and accuracy. However, invasive techniques may alter normal physiological function, thus generating a modality-based impact on vessel's permeability, which needs to be corrected for. Non-invasive approaches do not affect proper functionality of the inspected system, but lack in spatiotemporal resolution. Nevertheless, the benefit of medical imaging, even in pre-clinical phases, outweighs its disadvantages. The innovations in pre-clinical imaging and the development of novel processing techniques, have led to their implementation in clinical use as well. Specialized analyses of vessels' permeability add valuable information to standard anatomical inspections which do not take the latter into consideration.
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Affiliation(s)
- Yoash Chassidim
- Departments of Physiology & Cell Biology, Cognitive and Brain Sciences, The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Udi Vazana
- Departments of Physiology & Cell Biology, Cognitive and Brain Sciences, The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ofer Prager
- Departments of Physiology & Cell Biology, Cognitive and Brain Sciences, The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ronel Veksler
- Departments of Physiology & Cell Biology, Cognitive and Brain Sciences, The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Guy Bar-Klein
- Departments of Physiology & Cell Biology, Cognitive and Brain Sciences, The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Karl Schoknecht
- Department of Neurophysiology, Charite University of Medicine, Berlin, Germany
| | - Michael Fassler
- Departments of Physiology & Cell Biology, Cognitive and Brain Sciences, The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Svetlana Lublinsky
- Departments of Physiology & Cell Biology, Cognitive and Brain Sciences, The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ilan Shelef
- Medical Imaging Institute, Soroka Medical Center, Beer-Sheva, Israel
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Veksler R, Shelef I, Friedman A. Blood-brain barrier imaging in human neuropathologies. Arch Med Res 2014; 45:646-52. [PMID: 25453223 DOI: 10.1016/j.arcmed.2014.11.016] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Accepted: 11/20/2014] [Indexed: 01/22/2023]
Abstract
The blood-brain barrier (BBB) is essential for normal function of the brain, and its role in many brain pathologies has been the focus of numerous studies during the last decades. Dysfunction of the BBB is not only being shown in numerous brain diseases, but animal studies have indicated that it plays a direct key role in the genesis of neurovascular dysfunction and associated neurodegeneration. As such evidence accumulates, the need for robust and clinically applicable methods for minimally invasive assessment of BBB integrity is becoming urgent. This review provides an introduction to BBB imaging methods in the clinical scenario. First, imaging modalities are reviewed, with a focus on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We then proceed to review image analysis methods, including quantitative and semi-quantitative methods. The advantages and limitations of each approach are discussed, and future directions and questions are highlighted.
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Affiliation(s)
- Ronel Veksler
- Departments of Physiology and Cell Biology, Brain and Cognitive Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ilan Shelef
- Department of Medical Imaging, Soroka University Medical Center and the Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Alon Friedman
- Departments of Physiology and Cell Biology, Brain and Cognitive Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, Canada.
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Evaluation of IAUGC indices and two DCE-MRI pharmacokinetic parameters assessed by two different theoretical algorithms in patients with brain tumors. Clin Imaging 2014; 38:808-14. [DOI: 10.1016/j.clinimag.2014.07.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2013] [Revised: 06/09/2014] [Accepted: 07/10/2014] [Indexed: 11/20/2022]
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