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Ghaznavi H, Maraghechi B, Zhang H, Zhu T, Laugeman E, Zhang T, Zhao T, Mazur TR, Darafsheh A. Quantitative use of cone-beam computed tomography in proton therapy: challenges and opportunities. Phys Med Biol 2025; 70:09TR01. [PMID: 40269645 DOI: 10.1088/1361-6560/adc86c] [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: 07/07/2024] [Accepted: 04/01/2025] [Indexed: 04/25/2025]
Abstract
The fundamental goal in radiation therapy (RT) is to simultaneously maximize tumor cell killing and healthy tissue sparing. Reducing uncertainty margins improves normal tissue sparing, but generally requires advanced techniques. Adaptive RT (ART) is a compelling technique that leverages daily imaging and anatomical information to support reduced margins and to optimize plan quality for each treatment fraction. An especially exciting avenue for ART is proton therapy (PT), which aims to combine daily plan re-optimization with the unique advantages provided by protons, including reduced integral dose and near-zero dose deposition distal to the target along the beam direction. A core component for ART is onboard image guidance, and currently two options are available on proton systems, including cone-beam computed tomography (CBCT) and CT-on-rail (CToR) imaging. While CBCT suffers from poorer image quality compared to CToR imaging, CBCT platforms can be more easily integrated with PT systems and thus may support more streamlined adaptive proton therapy (APT). In this review, we present current status of CBCT application to proton therapy dose evaluation and plan adaptation, including progress, challenges and future directions.
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Affiliation(s)
- Hamid Ghaznavi
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
| | - Borna Maraghechi
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
- Department of Radiation Oncology, City of Hope Cancer Center, Irvine, CA 92618, United States of America
| | - Hailei Zhang
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
| | - Tong Zhu
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
| | - Eric Laugeman
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
| | - Tiezhi Zhang
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
| | - Tianyu Zhao
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
| | - Thomas R Mazur
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
| | - Arash Darafsheh
- Department of Radiation Oncology, WashU Medicine, St. Louis, MO 63110, United States of America
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Park HJ, Choi H, Ryu RR. Double low protocol in pediatric abdominal CT for evaluating right lower quadrant pain. Jpn J Radiol 2025:10.1007/s11604-025-01766-w. [PMID: 40156737 DOI: 10.1007/s11604-025-01766-w] [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: 09/13/2024] [Accepted: 02/27/2025] [Indexed: 04/01/2025]
Abstract
PURPOSE In pediatric patients, minimizing radiation and contrast media exposure without compromising diagnostic accuracy is paramount. Double low protocol, which utilizes a low dose contrast concentration and low tube voltage, could be a safer alternative. We compare diagnostic efficacy of double low protocol (Group A, 240 mgI/ml + 80 kVp) with conventional protocol (Group B, 350 mgI/ml + 120 kVp) in pediatric patients (< 10 years) presenting with abdominal pain and suspected acute appendicitis. MATERIALS AND METHODS This retrospective study included 121 pediatric patients who underwent enhanced abdominal CT between January 2019 and February 2023: 62 with Group A and 59 with Group B. We compared radiation dose, iodine load, and quantitative image quality parameters. Two radiologists independently assessed diagnostic image quality on a 5-point scale, visualization of the appendix, and diagnostic performance for acute appendicitis and its complications. RESULTS There were no significant differences in mean age (7.6 ± 2.0 vs. 7.6 ± 2.1, p = 0.956), body weight (31.4 ± 11.2 kg vs. 31.7 ± 11.4 kg, p = 0.972), and contrast media volume used (59.3 ± 21.0 ml vs. 65.0 ± 20.0 ml, p = 135) between the two groups. However, effective dose and iodine load used were significantly lower in Group A compared to Group B (2.7 ± 1.1 mSv vs. 4.3 ± 1.5 mSv and vs. 12.7 ± 4.6gI vs.18.6 ± 6.7gI, all p < 0.001). Although diagnostic image quality, noise and signal-to-noise ratio were significantly lower in Group A, visualization of the appendix (p = 0.853) and diagnostic accuracy for appendicitis were comparable between the two groups (98.4% vs. 94.9%, p = 0.284). DISCUSSION The double low protocol offers an effective alternative for evaluating pediatric patients requiring enhanced abdomen CT, achieving comparable diagnostic performance while significantly reducing radiation dose. We believe that our findings support safer CT acquisition practices for pediatric patients requiring enhanced CT imaging.
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Affiliation(s)
- Hyun Jeong Park
- Department of Radiology, Chung-Ang University Hospitall, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
| | - Hyewon Choi
- Department of Radiology, Chung-Ang University Hospitall, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Rae Rim Ryu
- Department of Radiology, Chung-Ang University Hospitall, Chung-Ang University College of Medicine, Seoul, Republic of Korea
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Xiao S, Zeng S, Kou Y. Clinical value and radiographic features of low dose CT scans compared to X rays in diagnosing mycoplasma pneumonia in children. Sci Rep 2025; 15:9162. [PMID: 40097591 PMCID: PMC11914611 DOI: 10.1038/s41598-025-94006-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Accepted: 03/11/2025] [Indexed: 03/19/2025] Open
Abstract
The aim of this article is to explore the lung characteristics and diagnostic value of X-ray and low-dose computed tomography (LDCT) in children with mycoplasma pneumoniae pneumonia (MPP). A total of 807 suspected children with MPP admitted to outpatient and inpatient departments from August 2023 to November 2023, were selected and divided into X-ray group (n = 389) and LDCT group (n = 418) according to the examination method. The two groups received chest X-ray examination and LDCT examination, respectively. Using pathogen detection results as the gold standard, we compared the imaging visible symptoms, diagnostic rate, sensitivity, specificity, diagnostic accuracy, and missed diagnosis rate of the two examination methods. In the LDCT, the main manifestations were thickened lung markings, patchy and striped shadows, followed by ground glass-like shadows, interstitial infiltration of the lungs, tree-in-fog sign, bronchial wall thickening, pleural thickening, consolidation/atelectasis, air bronchogram sign, and branching linear opacities (tree-in-bud sign). The main manifestations of X-ray were thickening of lung markings, followed by pulmonary patchy shadows, interstitial infiltration, and consolidation/atelectasis. LDCT scans revealed 359 cases of pediatric MPP, with 337 true positives cases, 22 false negatives cases, 23 false positives cases, and 36 true negatives cases. On the other hand, X-ray examinations identified 308 cases of pediatric MPP, with 266 true positive cases, 42 false negative cases, 35 false positive cases, and 46 true negative cases. The diagnostic rate, sensitivity, specificity and diagnostic accuracy of LDCT scans were 80.62%, 93.87%, 61.02% and 89.23%, which were higher compared to X-ray examinations, with the values of 68.38%, 86.36%, 56.79% and 80.21%. The missed diagnosis rate for LDCT was 6.13%, which was lower than the 13.64% rate for X-ray. LDCT demonstrated higher visibility of diagnostic rate, sensitivity, specificity and diagnostic accuracy compared to X-ray examinations, and the missed diagnosis rate of LDCT was lower than that of X-ray. Additionally, the radiographic features of LDCT were more characteristic, with simpler procedure, greater clinical utility.
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Affiliation(s)
- Simin Xiao
- Radiology department, The First Affiliated Hospital of Traditional Chinese Medicine of Chengdu Medical College, XinDu Hospital of Traditional Chinese Medicine, Chengdu, China
| | - Siyuan Zeng
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yangbin Kou
- Radiology department, The First Affiliated Hospital of Traditional Chinese Medicine of Chengdu Medical College, XinDu Hospital of Traditional Chinese Medicine, Chengdu, China.
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Miftahuddin D, Prayitno AG, Hariyanto AP, Gani MRA, Endarko E. Evaluation of low-dose pediatric chest CT examination using in-house developed various age-size pediatric chest phantoms. Eur J Radiol 2024; 177:111599. [PMID: 38970995 DOI: 10.1016/j.ejrad.2024.111599] [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: 02/08/2024] [Revised: 04/03/2024] [Accepted: 07/01/2024] [Indexed: 07/08/2024]
Abstract
PURPOSE This study aims to develop Various Age-size Pediatric Chest Phantoms (VAPC) to evaluate low-dose protocol that approximates clinical conditions achieved by low organ-specific doses and optimal image quality among the challenges of pediatric size variations. METHODS Three original pediatric data aged 1, 4, and 7 years were used as a reference for developing VAPC phantoms. Six protocols, namely standard dose (STD) and low dose (low mA and low kV) reconstructed using Filtered Back Projection (FBP) and iterative reconstruction (IR) algorithms, were investigated. This study directly measured the lungs, heart, and spinal cord dose using LD-V1 film. Linearity, Modulation Transfer Function (MTF), Contrast to Noise Ratio (CNR), and Noise Power Spectrum (NPS) were evaluated to assess the CT image quality of the VAPC phantom. RESULTS This study found that the mean organ-specific dose was higher than CTDIvol. A Comparison of mean lung doses showed VAPC phantom 1 (y.o.) received 74.8% and 137.2% more doses than 4 (y.o.) and 7 (y.o.), respectively. Low kV produces a lower organ dose than low mA. The linearity of CT numbers is not biased at low doses. Differences in age measures significantly influenced organ-specific dose, MTF, CNR, and NPS. CONCLUSION Smaller pediatrics are still exposed to higher doses at low-dose examinations, whereas larger pediatrics have lower contrast resolution and increased image noise. CT number linearity is unbiased. The combination of low kV with FBP produces higher spatial resolution, while low mA with IR effectively reduces noise to detect low-contrast objects better.
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Affiliation(s)
- Dafa Miftahuddin
- Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS - Sukolilo Surabaya 600111, East Java, Indonesia
| | - Audiena Gelung Prayitno
- Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS - Sukolilo Surabaya 600111, East Java, Indonesia
| | - Aditya Prayugo Hariyanto
- Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS - Sukolilo Surabaya 600111, East Java, Indonesia
| | - M Roslan A Gani
- Department of Radiology, Dharmais Hospital National Cancer Center, Jakarta 11420, Indonesia
| | - Endarko Endarko
- Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS - Sukolilo Surabaya 600111, East Java, Indonesia.
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Ahn C, Kim JH. AntiHalluciNet: A Potential Auditing Tool of the Behavior of Deep Learning Denoising Models in Low-Dose Computed Tomography. Diagnostics (Basel) 2023; 14:96. [PMID: 38201404 PMCID: PMC10795730 DOI: 10.3390/diagnostics14010096] [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: 11/02/2023] [Revised: 12/14/2023] [Accepted: 12/30/2023] [Indexed: 01/12/2024] Open
Abstract
Gaining the ability to audit the behavior of deep learning (DL) denoising models is of crucial importance to prevent potential hallucinations and adversarial clinical consequences. We present a preliminary version of AntiHalluciNet, which is designed to predict spurious structural components embedded in the residual noise from DL denoising models in low-dose CT and assess its feasibility for auditing the behavior of DL denoising models. We created a paired set of structure-embedded and pure noise images and trained AntiHalluciNet to predict spurious structures in the structure-embedded noise images. The performance of AntiHalluciNet was evaluated by using a newly devised residual structure index (RSI), which represents the prediction confidence based on the presence of structural components in the residual noise image. We also evaluated whether AntiHalluciNet could assess the image fidelity of a denoised image by using only a noise component instead of measuring the SSIM, which requires both reference and test images. Then, we explored the potential of AntiHalluciNet for auditing the behavior of DL denoising models. AntiHalluciNet was applied to three DL denoising models (two pre-trained models, RED-CNN and CTformer, and a commercial software, ClariCT.AI [version 1.2.3]), and whether AntiHalluciNet could discriminate between the noise purity performances of DL denoising models was assessed. AntiHalluciNet demonstrated an excellent performance in predicting the presence of structural components. The RSI values for the structural-embedded and pure noise images measured using the 50% low-dose dataset were 0.57 ± 31 and 0.02 ± 0.02, respectively, showing a substantial difference with a p-value < 0.0001. The AntiHalluciNet-derived RSI could differentiate between the quality of the degraded denoised images, with measurement values of 0.27, 0.41, 0.48, and 0.52 for the 25%, 50%, 75%, and 100% mixing rates of the degradation component, which showed a higher differentiation potential compared with the SSIM values of 0.9603, 0.9579, 0.9490, and 0.9333. The RSI measurements from the residual images of the three DL denoising models showed a distinct distribution, being 0.28 ± 0.06, 0.21 ± 0.06, and 0.15 ± 0.03 for RED-CNN, CTformer, and ClariCT.AI, respectively. AntiHalluciNet has the potential to predict the structural components embedded in the residual noise from DL denoising models in low-dose CT. With AntiHalluciNet, it is feasible to audit the performance and behavior of DL denoising models in clinical environments where only residual noise images are available.
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Affiliation(s)
- Chulkyun Ahn
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea;
- ClariPi Research, ClariPi, Seoul 03088, Republic of Korea
| | - Jong Hyo Kim
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea;
- ClariPi Research, ClariPi, Seoul 03088, Republic of Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
- Department of Radiology, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon-si 16229, Republic of Korea
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Sato S, Urikura A, Mimatsu M, Miyamae Y, Jibiki Y, Yamashita M, Ishihara T. Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study. Phys Eng Sci Med 2023; 46:1713-1721. [PMID: 37725313 DOI: 10.1007/s13246-023-01331-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 09/06/2023] [Indexed: 09/21/2023]
Abstract
PURPOSE This study aimed to assess the image characteristics of deep-learning-based image processing software (DLIP; FCT PixelShine, FUJIFILM, Tokyo, Japan) and compare it with filtered back projection (FBP), model-based iterative reconstruction (MBIR), and deep-learning-based reconstruction (DLR). METHODS This phantom study assessed the object-specific spatial resolution (task-based transfer function [TTF]), noise characteristics (noise power spectrum [NPS]), and low-contrast detectability (low-contrast object-specific contrast-to-noise ratio [CNRLO]) at three different output doses (standard: 10 mGy; low: 3.9 mGy; ultralow: 2.0 mGy). The processing strength of DLIPFBP with A1, A4, and A9 was compared with those of FBP, MBIR, and DLR. RESULT The standard dose with high-contrast TTFs of DLIPFBP exceeded that of FBP. Low-contrast TTFs were comparable to or lower than that of FBP. The NPS peak frequency (fP) of DLIPFBP shifts to low spatial frequencies of up to 8.6% at ultralow doses compared to the standard FBP dose. MBIR shifted the most fP compared to FBP-a marked shift of up to 49%. DLIPFBP showed a CNRLO equal to or greater than that of DLR in standard or low doses. In contrast, the CNRLO of the DLIPFBP was equal to or lower than that of the DLR in ultralow doses. CONCLUSION DLIPFBP reduced image noise while maintaining a resolution similar to commercially available MBIR and DLR. The slight spatial frequency shift of fP in DLIPFBP contributed to the noise texture degradation suppression. The NPS suppression in the low spatial frequency range effectively improved the low-contrast detectability.
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Affiliation(s)
- Seiya Sato
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Atsushi Urikura
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
| | - Makoto Mimatsu
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Yuta Miyamae
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Yuji Jibiki
- Clinical Product Specialist Marketing Group, FUJIFILM Corporation, 7-3, Akasaka 9-Chome Minato-Ku, Tokyo, Japan
| | - Mami Yamashita
- Clinical Product Specialist Marketing Group, FUJIFILM Corporation, 7-3, Akasaka 9-Chome Minato-Ku, Tokyo, Japan
| | - Toshihiro Ishihara
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
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Bai H, Su M, Pang C, Xiong Z, Xia B, Zhao D, Li C, Mo Z, Gao F. An image reconstruction method for transmission computed tomography with the constraint of the linear attenuation coefficients. Appl Radiat Isot 2023; 202:111062. [PMID: 37797448 DOI: 10.1016/j.apradiso.2023.111062] [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: 02/08/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/07/2023]
Abstract
For the reconstructed image of transmission computed tomography, the linear attenuation coefficients of the diagnosed object may improve the image quality by adding additional constraint besides the projection data. In the present work, an image reconstruction method with the constraint of the linear attenuation coefficients is developed and two models including a classical numerical Shepp-Logan model and a Monte Carlo model are used to show the corresponding benefits. The results indicate that the number of the projection angles is potentially decreased to 1/3 of itself while the quality of the reconstructed image is not deteriorated.
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Affiliation(s)
- Huaiyong Bai
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Ming Su
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Chengguo Pang
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Zhonghua Xiong
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Binyuan Xia
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Deshan Zhao
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China.
| | - Chenguang Li
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Zhaohong Mo
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
| | - Fan Gao
- Institute of Materials, China Academy of Engineering Physics, Jiangyou, 621907, China
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Miyata H, Sonomoto K, Fukuyo S, Nakayamada S, Nakano K, Iwata S, Miyazaki Y, Kawabe A, Aoki T, Tanaka Y. Computed tomography for malignancy screening in patients with rheumatoid arthritis before initiation of disease modifying antirheumatic drug. Rheumatology (Oxford) 2023; 62:3339-3349. [PMID: 36782362 DOI: 10.1093/rheumatology/kead075] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/14/2023] [Accepted: 02/01/2023] [Indexed: 02/15/2023] Open
Abstract
OBJECTIVES This study aimed to clarify the usefulness of screening for malignancies using CT before the initiation of biologic and targeted synthetic DMARDs (b/tsDMARDs) in patients with active RA. METHODS We examined 2192 patients with RA who underwent plain CT scans prior to the initiation of b/tsDMARDs. The sensitivity for detecting malignancy was measured and compared with that of regular screening (physical examination and X-ray). We then evaluated the clinical characteristics, prognosis and treatment of patients with RA with concomitant malignancies. Additionally, we determined the incidence rate of malignancy in patients with RA who were initiated on b/tsDMARDs after CT screening. RESULTS Of the 2192 patients, 33 (1.5%) were diagnosed with malignancy after CT screening. Whereas regular screening detected only seven malignancies, CT screening further detected 26 (including 19 at the early stage). On the other hand, 86% of the malignancies detectable by regular screening were at an advanced stage. Patients diagnosed with early-stage malignancies received RA treatments that included b/tsDMARDs after curative resection; 80% of these patients achieved low disease activity after 1 year. This rate was comparable to the patients without malignancy detection after screening (70%). The 5 year incidence of malignancy after the initiation of b/tsDMARDs after CT screening was lower than that of the RA cohort without CT screening (standardized incidence ratio: 0.35). CONCLUSION Screening in patients with RA using CT before the initiation of b/tsDMARDs allows for the early detection and treatment of malignancy, resulting in safer and more stable b/tsDMARD treatments.
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Affiliation(s)
- Hiroko Miyata
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Koshiro Sonomoto
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
- Department of Clinical Nursing, School of Health Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Shunsuke Fukuyo
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
- Department of Rheumatology, Wakamatsu Hospital of the University of the Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Shingo Nakayamada
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Kazuhisa Nakano
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
- Department of Rheumatology, Kawasaki Medical School, Okayama, Japan
| | - Shigeru Iwata
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
- Department of Rheumatology, Wakayama Medical University, Wakayama, Japan
| | - Yusuke Miyazaki
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Akio Kawabe
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Yoshiya Tanaka
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
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Park JC, Song B, Liang X, Lu B, Tan J, Parisi A, Denbeigh J, Yaddanpudi S, Choi B, Kim JS, Furutani KM, Beltran CJ. A high-resolution cone beam computed tomography (HRCBCT) reconstruction framework for CBCT-guided online adaptive therapy. Med Phys 2023; 50:6490-6501. [PMID: 37690458 DOI: 10.1002/mp.16734] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/18/2023] [Accepted: 08/19/2023] [Indexed: 09/12/2023] Open
Abstract
BACKGROUND Kilo-voltage cone-beam computed tomography (CBCT) is a prevalent modality used for adaptive radiotherapy (ART) due to its compatibility with linear accelerators and ability to provide online imaging. However, the widely-used Feldkamp-Davis-Kress (FDK) reconstruction algorithm has several limitations, including potential streak aliasing artifacts and elevated noise levels. Iterative reconstruction (IR) techniques, such as total variation (TV) minimization, dictionary-based methods, and prior information-based methods, have emerged as viable solutions to address these limitations and improve the quality and applicability of CBCT in ART. PURPOSE One of the primary challenges in IR-based techniques is finding the right balance between minimizing image noise and preserving image resolution. To overcome this challenge, we have developed a new reconstruction technique called high-resolution CBCT (HRCBCT) that specifically focuses on improving image resolution while reducing noise levels. METHODS The HRCBCT reconstruction technique builds upon the conventional IR approach, incorporating three components: the data fidelity term, the resolution preservation term, and the regularization term. The data fidelity term ensures alignment between reconstructed values and measured projection data, while the resolution preservation term exploits the high resolution of the initial Feldkamp-Davis-Kress (FDK) algorithm. The regularization term mitigates noise during the IR process. To enhance convergence and resolution at each iterative stage, we applied Iterative Filtered Backprojection (IFBP) to the data fidelity minimization process. RESULTS We evaluated the performance of the proposed HRCBCT algorithm using data from two physical phantoms and one head and neck patient. The HRCBCT algorithm outperformed all four different algorithms; FDK, Iterative Filtered Back Projection (IFBP), Compressed Sensing based Iterative Reconstruction (CSIR), and Prior Image Constrained Compressed Sensing (PICCS) methods in terms of resolution and noise reduction for all data sets. Line profiles across three line pairs of resolution revealed that the HRCBCT algorithm delivered the highest distinguishable line pairs compared to the other algorithms. Similarly, the Modulation Transfer Function (MTF) measurements, obtained from the tungsten wire insert on the CatPhan 600 physical phantom, showed a significant improvement with HRCBCT over traditional algorithms. CONCLUSION The proposed HRCBCT algorithm offers a promising solution for enhancing CBCT image quality in adaptive radiotherapy settings. By addressing the challenges inherent in traditional IR methods, the algorithm delivers high-definition CBCT images with improved resolution and reduced noise throughout each iterative step. Implementing the HR CBCT algorithm could significantly impact the accuracy of treatment planning during online adaptive therapy.
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Affiliation(s)
- Justin C Park
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Bongyong Song
- Department of Radiation Oncology, University of California San Diego, San Diego, California, USA
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Bo Lu
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Jun Tan
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Alessio Parisi
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Janet Denbeigh
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | | | - Byongsu Choi
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
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Brady SL. Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved. Br J Radiol 2023; 96:20220915. [PMID: 37102695 PMCID: PMC10546449 DOI: 10.1259/bjr.20220915] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 04/28/2023] Open
Abstract
CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.
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Abstract
In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for a 180° rotation to today's 0.24-second scan for a 360° rotation, CT technology continues to reinvent itself. This article describes key historical milestones in CT technology from the earliest days of CT to the present, with a look toward the future of this essential imaging modality. After a review of the beginnings of CT and its early adoption, the technical steps taken to decrease scan times-both per image and per examination-are reviewed. Novel geometries such as electron-beam CT and dual-source CT have also been developed in the quest for ever-faster scans and better in-plane temporal resolution. The focus of the past 2 decades on radiation dose optimization and management led to changes in how exposure parameters such as tube current and tube potential are prescribed such that today, examinations are more customized to the specific patient and diagnostic task than ever before. In the mid-2000s, CT expanded its reach from gray-scale to color with the clinical introduction of dual-energy CT. Today's most recent technical innovation-photon-counting CT-offers greater capabilities in multienergy CT as well as spatial resolution as good as 125 μm. Finally, artificial intelligence is poised to impact both the creation and processing of CT images, as well as automating many tasks to provide greater accuracy and reproducibility in quantitative applications.
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Affiliation(s)
- Cynthia H. McCollough
- Department of Radiology, Mayo Clinic, 200 First St SW Rochester, MN, United States 55905
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Tavares de Sousa H, Magro F. How to Evaluate Fibrosis in IBD? Diagnostics (Basel) 2023; 13:2188. [PMID: 37443582 DOI: 10.3390/diagnostics13132188] [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: 05/25/2023] [Revised: 06/21/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
In this review, we will describe the importance of fibrosis in inflammatory bowel disease (IBD) by discussing its distinct impact on Crohn's disease (CD) and ulcerative colitis (UC) through their translation to histopathology. We will address the existing knowledge on the correlation between inflammation and fibrosis and the still not fully explained inflammation-independent fibrogenesis. Finally, we will compile and discuss the recent advances in the noninvasive assessment of intestinal fibrosis, including imaging and biomarkers. Based on the available data, none of the available cross-sectional imaging (CSI) techniques has proved to be capable of measuring CD fibrosis accurately, with MRE showing the most promising performance along with elastography. Very recent research with radiomics showed encouraging results, but further validation with reliable radiomic biomarkers is warranted. Despite the interesting results with micro-RNAs, further advances on the topic of fibrosis biomarkers depend on the development of robust clinical trials based on solid and validated endpoints. We conclude that it seems very likely that radiomics and AI will participate in the future non-invasive fibrosis assessment by CSI techniques in IBD. However, as of today, surgical pathology remains the gold standard for the diagnosis and quantification of intestinal fibrosis in IBD.
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Affiliation(s)
- Helena Tavares de Sousa
- Gastroenterology Department, Algarve University Hospital Center, 8500-338 Portimão, Portugal
- ABC-Algarve Biomedical Center, University of Algarve, 8005-139 Faro, Portugal
| | - Fernando Magro
- Unit of Pharmacology and Therapeutics, Department of Biomedicine, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
- Department of Gastroenterology, São João University Hospital Center, 4200-319 Porto, Portugal
- CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
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Chun M, Choi JH, Kim S, Ahn C, Kim JH. Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study. PLoS One 2022; 17:e0271724. [PMID: 35857804 PMCID: PMC9299323 DOI: 10.1371/journal.pone.0271724] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 07/06/2022] [Indexed: 12/21/2022] Open
Abstract
While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.
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Affiliation(s)
- Minsoo Chun
- Department of Radiation Oncology, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Jin Hwa Choi
- Department of Radiation Oncology, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Sihwan Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Chulkyun Ahn
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- ClariPi Research, Seoul, Republic of Korea
| | - Jong Hyo Kim
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- ClariPi Research, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea
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Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure. ENTROPY 2022; 24:e24050740. [PMID: 35626623 PMCID: PMC9141439 DOI: 10.3390/e24050740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 11/17/2022]
Abstract
Iterative reconstruction of density pixel images from measured projections in computed tomography has attracted considerable attention. The ordered-subsets algorithm is an acceleration scheme that uses subsets of projections in a previously decided order. Several methods have been proposed to improve the convergence rate by permuting the order of the projections. However, they do not incorporate object information, such as shape, into the selection process. We propose a block-iterative reconstruction from sparse projection views with the dynamic selection of subsets based on an estimating function constructed by an extended power-divergence measure for decreasing the objective function as much as possible. We give a unified proposition for the inequality related to the difference between objective functions caused by one iteration as the theoretical basis of the proposed optimization strategy. Through the theory and numerical experiments, we show that nonuniform and sparse use of projection views leads to a reconstruction of higher-quality images and that an ordered subset is not the most effective for block-iterative reconstruction. The two-parameter class of extended power-divergence measures is the key to estimating an effective decrease in the objective function and plays a significant role in constructing a robust algorithm against noise.
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Omer H, Tamam N, Alameen S, Algadi S, Thanh Tai D, Sulieman A. Elimination of biological and physical artifacts in abdomen and brain computed tomography procedures using filtering techniques. Saudi J Biol Sci 2022; 29:2180-2186. [PMID: 35531247 PMCID: PMC9073048 DOI: 10.1016/j.sjbs.2021.11.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 11/18/2022] Open
Abstract
Filters reduce the noise at the expense of visual image quality. Digital interpretation of images prevents misinterpretation of the images due to blurring of the images. Dose reduction without compromising the diagnostic. Introduction Medical images are usually affected by biological and physical artifacts or noise, which reduces image quality and hence poses difficulties in visual analysis, interpretation and thus requires higher doses and increased radiographs repetition rate. Objectives This study aims at assessing image quality during CT abdomen and brain examinations using filtering techniques as well as estimating the radiogenic risk associated with CT abdomen and brain examinations. Materials and Methods The data were collected from the Radiology Department at Royal Care International (RCI) Hospital, Khartoum, Sudan. The study included 100 abdominal CT images and 100 brain CT images selected from adult patients. Filters applied are namely: Mean filter, Gaussian filter, Median filter and Minimum filter. In this study, image quality after denoising is measured based on the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Metric (SSIM). Results The results show that the images quality parameters become higher after applications of filters. Median filter showed improved image quality as interpreted by the measured parameters: PSNR and SSIM, and it is thus considered as a better filter for removing the noise from all other applied filters. Discussion The noise removed by the different filters applied to the CT images resulted in enhancing high quality images thereby effectively revealing the important details of the images without increasing the patients’ risks from higher doses. Conclusions Filtering and image reconstruction techniques not only reduce the dose and thus the radiation risks, but also enhances high quality imaging which allows better diagnosis.
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Affiliation(s)
- Hiba Omer
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
| | - Nissren Tamam
- Physics Department, College of Sciences, Princess Nourah bint Abdulrahman University, P.O Box 84428, Riyadh 11671, Saudi Arabia
- Corresponding author.
| | - Suhaib Alameen
- Sudan University of Science and Technology, College of Medical Radiologic Science, P.O.Box 1908, Khartoum, Sudan
| | - Sahar Algadi
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
| | - Duong Thanh Tai
- Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh 749000, Vietnam
| | - Abdelmoneim Sulieman
- Prince Sattam Bin Abdulaziz University, College of Applied Medical Sciences, Radiology and Medical Imaging Department, PO Box 422, Alkharj 11942, Saudi Arabia
- Basic Science Department, College of Medical Radiologic Science, Sudan University of Science and Technology, P.O.Box 1908, Khartoum 11111, Sudan
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Sartoretti T, Racine D, Mergen V, Jungblut L, Monnin P, Flohr TG, Martini K, Frauenfelder T, Alkadhi H, Euler A. Quantum Iterative Reconstruction for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lung. Diagnostics (Basel) 2022; 12:522. [PMID: 35204611 PMCID: PMC8871296 DOI: 10.3390/diagnostics12020522] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 02/06/2023] Open
Abstract
The aim of this study was to characterize image quality and to determine the optimal strength levels of a novel iterative reconstruction algorithm (quantum iterative reconstruction, QIR) for low-dose, ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung. Images were acquired on a clinical dual-source PCD-CT in the UHR mode and reconstructed with a sharp lung reconstruction kernel at different strength levels of QIR (QIR-1 to QIR-4) and without QIR (QIR-off). Noise power spectrum (NPS) and target transfer function (TTF) were analyzed in a cylindrical phantom. 52 consecutive patients referred for low-dose UHR chest PCD-CT were included (CTDIvol: 1 ± 0.6 mGy). Quantitative image quality analysis was performed computationally which included the calculation of the global noise index (GNI) and the global signal-to-noise ratio index (GSNRI). The mean attenuation of the lung parenchyma was measured. Two readers graded images qualitatively in terms of overall image quality, image sharpness, and subjective image noise using 5-point Likert scales. In the phantom, an increase in the QIR level slightly decreased spatial resolution and considerably decreased noise amplitude without affecting the frequency content. In patients, GNI decreased from QIR-off (202 ± 34 HU) to QIR-4 (106 ± 18 HU) (p < 0.001) by 48%. GSNRI increased from QIR-off (4.4 ± 0.8) to QIR-4 (8.2 ± 1.6) (p < 0.001) by 87%. Attenuation of lung parenchyma was highly comparable among reconstructions (QIR-off: -849 ± 53 HU to QIR-4: -853 ± 52 HU, p < 0.001). Subjective noise was best in QIR-4 (p < 0.001), while QIR-3 was best for sharpness and overall image quality (p < 0.001). Thus, our phantom and patient study indicates that QIR-3 provides the optimal iterative reconstruction level for low-dose, UHR PCD-CT of the lungs.
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Affiliation(s)
- Thomas Sartoretti
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland; (T.S.); (V.M.); (L.J.); (K.M.); (T.F.); (H.A.)
| | - Damien Racine
- Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV), University of Lausanne (UNIL), CH-1010 Lausanne, Switzerland; (D.R.); (P.M.)
| | - Victor Mergen
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland; (T.S.); (V.M.); (L.J.); (K.M.); (T.F.); (H.A.)
| | - Lisa Jungblut
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland; (T.S.); (V.M.); (L.J.); (K.M.); (T.F.); (H.A.)
| | - Pascal Monnin
- Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV), University of Lausanne (UNIL), CH-1010 Lausanne, Switzerland; (D.R.); (P.M.)
| | | | - Katharina Martini
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland; (T.S.); (V.M.); (L.J.); (K.M.); (T.F.); (H.A.)
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland; (T.S.); (V.M.); (L.J.); (K.M.); (T.F.); (H.A.)
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland; (T.S.); (V.M.); (L.J.); (K.M.); (T.F.); (H.A.)
| | - André Euler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, CH-8091 Zurich, Switzerland; (T.S.); (V.M.); (L.J.); (K.M.); (T.F.); (H.A.)
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Chaparian A, Asemanrafat M, Lotfi M, Rasekhi A. Impact of iterative reconstruction algorithms on image quality and radiation dose in computed tomography scan of patients with malignant pancreatic lesions. JOURNAL OF MEDICAL SIGNALS & SENSORS 2022; 12:69-75. [PMID: 35265468 PMCID: PMC8804595 DOI: 10.4103/jmss.jmss_81_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/06/2021] [Accepted: 02/03/2021] [Indexed: 12/24/2022]
Abstract
Background: The objective of this study was to investigate the influence of iterative reconstruction (IR) algorithm on radiation dose and image quality of computed tomography (CT) scans of patients with malignant pancreatic lesions by designing a new protocol. Methods: The pancreas CT was performed on 40 patients (23 males and 17 females) with a 160-slice CT scan machine. The pancreatic parenchymal phase was performed in two stages: one with a usual dose of radiation and the other one after using a reduced dose of radiation. The images obtained with usual dose were reconstructed with Filtered Back Projection (FBP) method (Protocol A); and the images obtained with the reduced dose were reconstructed with both FBP (Protocol B) and IR method (Protocol C). The quality of images and radiation dose were compared among the three protocols. Results: Image noise was significantly lower with Protocol C (10.80) than with Protocol A (14.98) and Protocol B (20.60) (P < 0.001). Signal-to-noise ratio and contrast-to-noise ratio were significantly higher with Protocol C than with Protocol A and Protocol B (P < 0.001). Protocol A and Protocol C were not significantly different in terms of image quality scores. Effective dose was reduced by approximately 48% in Protocol C compared with Protocol A (1.20 ± 0.53 mSv vs. 2.33 ± 0.86 mSv, P < 0.001). Conclusion: Results of this study showed that applying the IR method compared to the FBP method can improve objective image quality, maintain subjective image quality, and reduce the radiation dose of the patients undergo pancreas CT.
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Li Y, Jiang Y, Liu H, Yu X, Chen S, Ma D, Gao J, Wu Y. A phantom study comparing low-dose CT physical image quality from five different CT scanners. Quant Imaging Med Surg 2022; 12:766-780. [PMID: 34993117 PMCID: PMC8666789 DOI: 10.21037/qims-21-245] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 07/29/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND To systematically evaluate the physical image quality of low-dose computed tomography (LDCT) on CT scanners from 5 different manufacturers using a phantom model. METHODS CT images derived from a Catphan 500 phantom were acquired using manufacturer-specific iterative reconstruction (IR) algorithms and deep learning image reconstruction (DLIR) on CT scanners from 5 different manufacturers and compared using filtered back projection with 2 radiation doses of 0.25 and 0.75 mGy. Image high-contrast spatial resolution and image noise were objectively characterized by modulation transfer function (MTF) and noise power spectrum (NPS). Image high-contrast spatial resolution and image low-contrast detectability were compared directly by visual evaluation. CT number linearity and image uniformity were compared with intergroup differences using one-way analysis of variance (ANOVA). RESULTS The CT number linearity of 4 insert materials were as follows: acrylic (95% CI: 120.35 to 121.27; P=0.134), low-density polyethylene (95% CI: -98.43 to -97.43; P=0.070), air (95% CI: -996.16 to -994.51; P=0.018), and Teflon (95% CI: 984.40 to 986.87; P=0.883). The image uniformity values of GE Healthcare (95% CI: 3.24 to 3.83; P=0.138), Philips (95% CI: 2.62 to 3.70; P=0.299), Siemens (95% CI: 2.10 to 3.59; P=0.054), Minfound (95% CI: 2.35 to 3.65; P=0.589), and Neusoft (95% CI: 2.63 to 3.37; P=0.900) were evaluated and found to be within ±4 Hounsfield units (HU), with a range of 0.99-2.76 HU for standard deviations. There was no statistically significant difference in CT number linearity and image uniformity across the 5 CT scanners under different radiation doses with IR and DLIR algorithms (P>0.05). The resolution level at 10% MTF was 6.98 line-pairs-per-centimeter (lp/cm) on average, which was similar to the subjective evaluation results (mostly up to 7 lp/cm). DLIR at all 3 levels had the highest 50% MTF values among all reconstruction algorithms. For image low-contrast detectability, the minimum diameter of distinguishable contrast holes reached 4 mm at a 0.5% resolution. Increasing the radiation dose and IR strength reduced the image noise and NPS curve peak frequency while improving image low-contrast detectability. CONCLUSIONS This study demonstrated that the image quality of CT scanners from 5 different manufacturers in LDCT is comparable and that the CT number linearity is unbiased and can contribute to accurate bone mineral density quantification.
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Affiliation(s)
- Yali Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yaojun Jiang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Huilong Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xi Yu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Sihui Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Duoshan Ma
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Wu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Kulkarni CB, Pullara SK, Prabhu NK, Patel S, Suresh A, Moorthy S. Comparison of Knowledge-based Iterative Model Reconstruction (IMR) with Hybrid Iterative Reconstruction (iDose 4) Techniques for Evaluation of Hepatocellular Carcinomas Using Computed Tomography. Acad Radiol 2021; 28 Suppl 1:S29-S36. [PMID: 32950385 DOI: 10.1016/j.acra.2020.08.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 02/08/2023]
Abstract
RATIONALE AND OBJECTIVES To compare tumor conspicuity of small hepatocellular carcinomas (HCCs) and image quality on knowledge-based iterative model reconstruction low-dose computed tomography (IMR-LDCT) with hybrid iterative reconstruction standard-dose CT (iDose4-SDCT). METHODS Thirty-two patients (mean age 61.9 ± 9.7 years; male:female 27:5; mean body mass index 25.6 ± 3.8 kg/m2) with cirrhosis and 40 HCCs in IMR-LDCT group and 33 patients (mean age 60.1 ± 7.4 years; male:female 28:5; body mass index 26.7 ± 3.2 kg/m2) with cirrhosis and 40 HCCs in iDose4-SDCT group were included in this retrospective study. Objective analysis of reconstructed iDose4 and IMR images was done for contrast-to-noise ratio of HCCs (CNRHCC), image noise, signal-to-noise ratio of portal vein (SNRPV), and inferior vena cava (SNRIVC). Subjective analysis of tumor conspicuity and image quality was done by two independent reviewers in a blinded manner. Mean volume CT dose index, dose length product, and effective dose for both groups were compared. RESULTS The CNRHCC was significantly higher in IMR-LDCT compared to iDose4-SDCT in both arterial phase (AP), p < 0.0001, and delayed phase (DP), p < 0.0001. Image noise was significantly lower in IMR-LDCT compared to iDose4-SDCT in AP, portal venous phase, and DP with p < 0.0001. IMR-LDCT showed significantly higher SNRPV (p < 0.0001) and SNRIVC (p < 0.0001) compared to iDose4-SDCT. On subjective analysis, IMR-LDCT images showed better image quality in AP, portal venous phase, and DP and better tumor conspicuity in AP and DP. IMR-LDCT (21.4 ± 4.6 mSv) achieved 36.9% reduction in the effective dose compared to iDose4-SDCT (33.9 ± 6.2 mSv). CONCLUSION IMR algorithm provides better image quality and tumor conspicuity with considerable decrease in image noise compared to iDose4 reconstruction technique even on LDCT.
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Computed Tomography Techniques, Protocols, Advancements, and Future Directions in Liver Diseases. Magn Reson Imaging Clin N Am 2021; 29:305-320. [PMID: 34243919 DOI: 10.1016/j.mric.2021.05.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Computed tomography (CT) is often performed as the initial imaging study for the workup of patients with known or suspected liver disease. Our article reviews liver CT techniques and protocols in clinical practice along with updates on relevant CT advances, including wide-detector CT, radiation dose optimization, and multienergy scanning, that have already shown clinical impact. Particular emphasis is placed on optimizing the late arterial phase of enhancement, which is critical to evaluation of hepatocellular carcinoma. We also discuss emerging techniques that may soon influence clinical care.
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Kasai R, Yamaguchi Y, Kojima T, Abou Al-Ola OM, Yoshinaga T. Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1005. [PMID: 34441145 PMCID: PMC8394634 DOI: 10.3390/e23081005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/21/2021] [Accepted: 07/24/2021] [Indexed: 12/03/2022]
Abstract
The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback-Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters.
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Affiliation(s)
- Ryosuke Kasai
- Graduate School of Health Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8509, Japan;
| | - Yusaku Yamaguchi
- Shikoku Medical Center for Children and Adults, National Hospital Organization, 2-1-1 Senyu, Zentsuji 765-8507, Japan;
| | - Takeshi Kojima
- Institute of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8509, Japan;
| | | | - Tetsuya Yoshinaga
- Institute of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8509, Japan;
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Han R, Li L, Yang P, Zhang F, Gao X. A novel constrained reconstruction model towards high-resolution subtomogram averaging. Bioinformatics 2021; 37:1616-1626. [PMID: 31617571 DOI: 10.1093/bioinformatics/btz787] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 08/12/2019] [Accepted: 10/14/2019] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION Electron tomography (ET) offers a unique capacity to image biological structures in situ. However, the resolution of ET reconstructed tomograms is not comparable to that of the single-particle cryo-EM. If many copies of the object of interest are present in the tomograms, their structures can be reconstructed in the tomogram, picked, aligned and averaged to increase the signal-to-noise ratio and improve the resolution, which is known as the subtomogram averaging. To date, the resolution improvement of the subtomogram averaging is still limited because each reconstructed subtomogram is of low reconstruction quality due to the missing wedge issue. RESULTS In this article, we propose a novel computational model, the constrained reconstruction model (CRM), to better recover the information from the multiple subtomograms and compensate for the missing wedge issue in each of them. CRM is supposed to produce a refined reconstruction in the final turn of subtomogram averaging after alignment, instead of directly taking the average. We first formulate the averaging method and our CRM as linear systems, and prove that the solution space of CRM is no larger, and in practice much smaller, than that of the averaging method. We then propose a sparse Kaczmarz algorithm to solve the formulated CRM, and further extend the solution to the simultaneous algebraic reconstruction technique (SART). Experimental results demonstrate that CRM can significantly alleviate the missing wedge issue and improve the final reconstruction quality. In addition, our model is robust to the number of images in each tilt series, the tilt range and the noise level. AVAILABILITY AND IMPLEMENTATION The codes of CRM-SIRT and CRM-SART are available at https://github.com/icthrm/CRM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Renmin Han
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Lun Li
- High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, 100190 Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Peng Yang
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Fa Zhang
- High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, 100190 Beijing, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Sugawara H, Yoshikawa T, Kunimatsu A, Akai H, Yasaka K, Abe O. Detectability of pancreatic lesions by low-dose unenhanced computed tomography using iterative reconstruction. Eur J Radiol 2021; 141:109776. [PMID: 34029934 DOI: 10.1016/j.ejrad.2021.109776] [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: 02/12/2021] [Revised: 04/09/2021] [Accepted: 05/11/2021] [Indexed: 11/19/2022]
Abstract
OBJECTIVES To investigate the detectability of pancreatic cystic lesions and main pancreatic duct dilation by low-dose unenhanced computed tomography (CT). MATERIAL AND METHODS This study included 2684 patients who underwent low-dose unenhanced CT using iterative reconstruction and magnetic resonance imaging (MRI) as a part of a health-screening program between February 1, 2019 and December 31, 2019. Patients diagnosed with pancreatic cystic lesions and/or dilatations of the main pancreatic duct on MRI were identified. Detection rates by low dose CT in terms of lesion size were tested for significance by Fisher's exact test. RESULTS Of the 2684 patients, 558 (20.8 %) had pancreatic cystic lesions and 22 (0.8 %) had main pancreatic duct dilatation on MRI. The low-dose CT detection rates among the pancreatic cystic lesions were as follows: 1-9-mm cysts, three (0.65 %) of 461; 10-19-mm cysts, 17 (21.25 %) of 80, and ≥20-mm cysts, eight (47.06 %) of 17. The detection rates were significantly higher in the 10-19-mm and the ≥20-mm cyst group than in the 1-9-mm cyst group (p < 0.001). The detection rates among the main pancreatic duct dilatations were as follows: 3-5-mm dilatations, two (11.76 %) of 17 and ≥6-mm dilatations, four (80 %) of five, which were significantly higher rates than that for the 3-5-mm dilatations (p = 0.009). CONCLUSION Small pancreatic cysts and slight main pancreatic duct dilatation were practically undetectable by low-dose unenhanced CT. The application of a low-dose CT protocol as a screening tool in the detection of pancreatic abnormalities is not recommended.
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Affiliation(s)
- Haruto Sugawara
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan.
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Akai
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Koichiro Yasaka
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Lange I, Alikhani B, Wacker F, Raatschen HJ. Intraindividual variation of dose parameters in oncologic CT imaging. PLoS One 2021; 16:e0250490. [PMID: 33891632 PMCID: PMC8064522 DOI: 10.1371/journal.pone.0250490] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 04/08/2021] [Indexed: 11/28/2022] Open
Abstract
The objective of this study is to identify essential aspects influencing radiation dose in computed tomography [CT] of the chest, abdomen and pelvis by intraindividual comparison of imaging parameters and patient related factors. All patients receiving at least two consecutive CT examinations for tumor staging or follow-up within a period of 22 months were included in this retrospective study. Different CT dose estimates (computed tomography dose index [CTDIvol], dose length product [DLP], size-specific dose estimate [SSDE]) were correlated with patient’s body mass index [BMI], scan length and technical parameters (tube current, tube voltage, pitch, noise level, level of iterative reconstruction). Repeated-measures-analysis was initiated with focus on response variables (CTDIvol, DLP, SSDE) and possible factors (age, BMI, noise, scan length, peak kilovoltage [kVp], tube current, pitch, adaptive statistical iterative reconstruction [ASIR]). A univariate-linear-mixed-model with repeated-measures-analysis followed by Bonferroni adjustments was used to find associations between CT imaging parameters, BMI and dose estimates followed by a subsequent multivariate-mixed-model with repeated-measures-analysis with Bonferroni adjustments for significant parameters. A p-value <0.05 was considered statistically significant. We found all dose estimates in all imaging regions were substantially affected by tube current. The iterative reconstruction significantly influenced all dose estimates in the thoracoabdominopelvic scans as well as DLP and SSDE in chest-CT. Pitch factor affected all dose parameters in the thoracoabdominopelvic CT group. These results provide further evidence that tube current has a pivotal role and potential in radiation dose management. The use of iterative reconstruction algorithms can substantially decrease radiation dose especially in thoracoabdominopelvic and chest-CT-scans. Pitch factor should be kept at a level of ≥1.0 in order to reduce radiation dose.
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Affiliation(s)
- Isabel Lange
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
- * E-mail:
| | - Babak Alikhani
- Center for Radiology and Nuclear Medicine, Diakovere Henriettenstift, Hannover, Germany
| | - Frank Wacker
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Hans-Juergen Raatschen
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
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Cheng Y, Han Y, Li J, Fan G, Cao L, Li J, Jia X, Yang J, Guo J. Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography. Br J Radiol 2021; 94:20201291. [PMID: 33571034 DOI: 10.1259/bjr.20201291] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
OBJECTIVES To compare the image quality of low-dose CT urography (LD-CTU) using deep learning image reconstruction (DLIR) with conventional CTU (C-CTU) using adaptive statistical iterative reconstruction (ASIR-V). METHODS This was a prospective, single-institutional study using the excretory phase CTU images for analysis. Patients were assigned to the LD-DLIR group (100kV and automatic mA modulation for noise index (NI) of 23) and C-ASIR-V group (100kV and NI of 10) according to the scan protocols in the excretory phase. Two radiologists independently assessed the overall image quality, artifacts, noise and sharpness of urinary tracts. Additionally, the mean CT attenuation, signal-to-noise ratio (SNR) and contrast-to-noise (CNR) in the urinary tracts were evaluated. RESULTS 26 patients each were included in the LD-DLIR group (10 males and 16 females; mean age: 57.23 years, range: 33-76 years) and C-ASIR-V group (14 males and 12 females; mean age: 60 years, range: 33-77 years). LD-DLIR group used a significantly lower effective radiation dose compared with the C-ASIR-V group (2.01 ± 0.44 mSv vs 6.9 ± 1.46 mSv, p < 0.001). LD-DLIR group showed good overall image quality with average score >4 and was similar to that of the C-ASIR-V group. Both groups had adequate and similar attenuation value, SNR and CNR in most segments of urinary tracts. CONCLUSION It is feasibility to provide comparable image quality while reducing 71% radiation dose in low-dose CTU with a deep learning image reconstruction algorithm compared to the conventional CTU with ASIR-V. ADVANCES IN KNOWLEDGE (1) CT urography with deep learning reconstruction algorithm can reduce the radiation dose by 71% while still maintaining image quality.
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Affiliation(s)
- Yannan Cheng
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Yangyang Han
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Jianying Li
- GE Healthcare, Computed Tomography Research Center, Beijing, 100176, PR China
| | - Ganglian Fan
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Le Cao
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Junjun Li
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Xiaoqian Jia
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Jian Yang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Jianxin Guo
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
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Nicolan B, Greffier J, Dabli D, de Forges H, Arcis E, Al Zouabi N, Larbi A, Beregi JP, Frandon J. Diagnostic performance of ultra-low dose versus standard dose CT for non-traumatic abdominal emergencies. Diagn Interv Imaging 2021; 102:379-387. [PMID: 33714689 DOI: 10.1016/j.diii.2021.02.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/09/2021] [Accepted: 02/18/2021] [Indexed: 12/29/2022]
Abstract
PURPOSE The purpose of this study was to compare the diagnostic performance of ultra-low dose (ULD) to that of standard (STD) computed tomography (CT) for the diagnosis of non-traumatic abdominal emergencies using clinical follow-up as reference standard. MATERIALS AND METHODS All consecutive patients requiring emergency abdomen-pelvic CT examination from March 2017 to September 2017 were prospectively included. ULD and STD CTs were acquired after intravenous administration iodinated contrast medium (portal phase). CT acquisitions were performed at 125mAs for STD and 55mAs for ULD. Diagnostic performance was retrospectively evaluated on ULD and STD CTs using clinical follow-up as a reference diagnosis. RESULTS A total of 308 CT examinations from 308 patients (145 men; mean age 59.1±20.7 (SD) years; age range: 18-96 years) were included; among which 241/308 (78.2%) showed abnormal findings. The effective dose was significantly lower with the ULD protocol (1.55±1.03 [SD] mSv) than with the STD (3.67±2.56 [SD] mSv) (P<0.001). Sensitivity was significantly lower for the ULD protocol (85.5% [95%CI: 80.4-89.4]) than for the STD (93.4% [95%CI: 89.4-95.9], P<0.001) whereas specificities were similar (94.0% [95%CI: 85.1-98.0] vs. 95.5% [95%CI: 87.0-98.9], respectively). ULD sensitivity was equivalent to STD for bowel obstruction and colitis/diverticulitis (96.4% [95%CI: 87.0-99.6] and 86.5% [95%CI: 74.3-93.5] for ULD vs. 96.4% [95%CI: 87.0-99.6] and 88.5% [95%CI: 76.5-94.9] for STD, respectively) but lower for appendicitis, pyelonephritis, abscesses and renal colic (75.0% [95%CI: 57.6-86.9]; 77.3% [95%CI: 56.0-90.1]; 90.5% [95%CI: 69.6-98.4] and 85% [95%CI: 62.9-95.4] for ULD vs. 93.8% [95%CI: 78.6-99.2]; 95.5% [95%CI: 76.2-100.0]; 100.0% [95%CI: 81.4-100.0] and 100.0% [95%CI: 80.6-100.0] for STD, respectively). Sensitivities were significantly different between the two protocols only for appendicitis (P=0.041). CONCLUSION In an emergency context, for patients with non-traumatic abdominal emergencies, ULD-CT showed inferior diagnostic performance compared to STD-CT for most abdominal conditions except for bowel obstruction and colitis/diverticulitis detection.
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Affiliation(s)
- Basien Nicolan
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, EA 2415, 30000 Nîmes, France
| | - Joël Greffier
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, EA 2415, 30000 Nîmes, France
| | - Djamel Dabli
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, EA 2415, 30000 Nîmes, France
| | - Hélène de Forges
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, EA 2415, 30000 Nîmes, France
| | - Elise Arcis
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, EA 2415, 30000 Nîmes, France
| | - Nadir Al Zouabi
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, EA 2415, 30000 Nîmes, France
| | - Ahmed Larbi
- ISERIS imagerie médicale, 34000 Montpellier, France
| | - Jean-Paul Beregi
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, EA 2415, 30000 Nîmes, France
| | - Julien Frandon
- Department of Medical Imaging, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, EA 2415, 30000 Nîmes, France.
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Vandecaveye V, Amant F, Lecouvet F, Van Calsteren K, Dresen RC. Imaging modalities in pregnant cancer patients. Int J Gynecol Cancer 2021; 31:423-431. [PMID: 33649009 PMCID: PMC7925814 DOI: 10.1136/ijgc-2020-001779] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 12/13/2022] Open
Abstract
Cancer during pregnancy is increasingly diagnosed due to the trend of delaying pregnancy to a later age and probably also because of increased use of non-invasive prenatal testing for fetal aneuploidy screening with incidental finding of maternal cancer. Pregnant women pose higher challenges in imaging, diagnosis, and staging of cancer. Physiological tissue changes related to pregnancy makes image interpretation more difficult. Moreover, uncertainty about the safety of imaging modalities, fear of (unnecessary) fetal radiation, and lack of standardized imaging protocols may result in underutilization of the necessary imaging tests resulting in suboptimal staging. Due to the absence of radiation exposure, ultrasound and MRI are obvious first-line imaging modalities for detailed locoregional disease assessment. MRI has the added advantage of a more reproducible comprehensive organ or body region assessment, the ability of distant staging through whole-body evaluation, and the combination of anatomical and functional information by diffusion-weighted imaging which obviates the need for a gadolinium-based contrast-agent. Imaging modalities with inherent radiation exposure such as CT and nuclear imaging should only be performed when the maternal benefit outweighs fetal risk. The cumulative radiation exposure should not exceed the fetal radiation threshold of 100 mGy. Imaging should only be performed when necessary for diagnosis and likely to guide or change management. Radiologists play an important role in the multidisciplinary team in order to select the most optimal imaging strategies that balance maternal benefit with fetal risk and that are most likely to guide treatment decisions. Our aim is to provide an overview of possibilities and concerns in current clinical applications and developments in the imaging of patients with cancer during pregnancy.
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Affiliation(s)
- Vincent Vandecaveye
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
- Department of Imaging & Pathology, KU Leuven, Leuven, Belgium
| | - Frédéric Amant
- Department of Gynaecology and Obstetrics, University Hospitals Leuven, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
- Center for Gynecological Oncology, Academic Medical Centre Amsterdam-University of Amsterdam and The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Frédéric Lecouvet
- Department of Radiology, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Kristel Van Calsteren
- Department of Gynaecology and Obstetrics, University Hospitals Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Raphaëla Carmen Dresen
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
- Department of Imaging & Pathology, KU Leuven, Leuven, Belgium
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Hasan AM, Mohebbian MR, Wahid KA, Babyn P. Hybrid-Collaborative Noise2Noise Denoiser for Low-Dose CT Images. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3002178] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Nam JG, Hong JH, Kim DS, Oh J, Goo JM. Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction. Eur Radiol 2021; 31:5533-5543. [PMID: 33555354 DOI: 10.1007/s00330-021-07712-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 12/23/2020] [Accepted: 01/21/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen. METHODS One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDIvol: chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case. RESULTS The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 ± 2.81, 14.8 ± 2.56, and 12.3 ± 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 ± 0.23 vs. 2.87 ± 0.26; p < .001), while DLIR showed better spatial resolution (2.60 ± 0.34 vs. 2.44 ± 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently. CONCLUSION With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers. KEY POINTS • With < 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm-reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.
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Affiliation(s)
- Ju Gang Nam
- Department of Radiology, Seoul National University Hospital and College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
| | - Jung Hee Hong
- Department of Radiology, Seoul National University Hospital and College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
| | - Da Som Kim
- Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Jiseon Oh
- Department of Radiology, Seoul National University Hospital and College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital and College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea. .,Cancer Research Institute, Seoul National University College of Medicine, 03080, Seoul, Republic of Korea.
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Review of Technical Advancements and Clinical Applications of Photon-counting Computed Tomography in Imaging of the Thorax. J Thorac Imaging 2021; 36:84-94. [PMID: 33399350 DOI: 10.1097/rti.0000000000000569] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Photon-counting computed tomography (CT) is a developing technology that has the potential to address some limitations of CT imaging and bring about improvements and potentially new applications to this field. Photon-counting detectors have a fundamentally different detection mechanism from conventional CT energy-integrating detectors that can improve dose efficiency, spatial resolution, and energy-discrimination capabilities. In the past decade, promising human studies have been reported in the literature that have demonstrated benefits of this relatively new technology for various clinical applications. In this review, we provide a succinct description of the photon-counting detector technology and its detection mechanism in comparison with energy-integrating detectors in a manner understandable for clinicians and radiologists, introduce benefits and some of the existing challenges present in this technology, and provide an overview of the current status and potential clinical applications of this technology in imaging of the thorax by providing example images acquired with an investigational whole-body photon-counting CT scanner.
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The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting. Clin Radiol 2020; 76:155.e15-155.e23. [PMID: 33220941 DOI: 10.1016/j.crad.2020.10.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 10/23/2020] [Indexed: 11/22/2022]
Abstract
AIM To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V). MATERIALS AND METHODS Thirty-six patients were evaluated retrospectively. All patients underwent contrast-enhanced chest CT and thin-section images were reconstructed using filtered back projection (FBP); ASiR-V (60% and 100% blending setting); and DLIR (low, medium, and high settings). Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated objectively. Two independent radiologists evaluated ASiR-V 60% and DLIR subjectively, in comparison with FBP, on a five-point scale in terms of noise, streak artefact, lymph nodes, small vessels, and overall image quality on a mediastinal window setting (width 400 HU, level 60 HU). In addition, image texture of ASiR-Vs (60% and 100%) and DLIR-high was analysed subjectively. RESULTS Compared with ASiR-V 60%, DLIR-med and DLIR-high showed significantly less noise, higher SNR, and higher CNR (p<0.0001). DLIR-high and ASiR-V 100% were not significantly different regarding noise (p=0.2918) and CNR (p=0.0642). At a higher DLIR setting, noise was lower and SNR and CNR were higher (p<0.0001). DLIR-high showed the best subjective scores for noise, streak artefact, and overall image quality (p<0.0001). Compared with ASiR-V 60%, DLIR-med and DLIR-high scored worse in the assessment of small vessels (p<0.0001). The image texture of DLIR-high was significantly finer than that of ASIR-Vs (p<0.0001). CONCLUSIONS DLIR-high improved the objective parameters and subjective image quality by reducing noise and streak artefacts and providing finer image texture.
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Shin YJ, Chang W, Ye JC, Kang E, Oh DY, Lee YJ, Park JH, Kim YH. Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm. Korean J Radiol 2020; 21:356-364. [PMID: 32090528 PMCID: PMC7039719 DOI: 10.3348/kjr.2019.0413] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 11/07/2019] [Indexed: 12/30/2022] Open
Abstract
Objective To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). Materials and Methods One hundred routine-dose (RD) abdominal CT studies reconstructed using FBP were used to train the DLA. Simulated CT images were made at dose levels of 13%, 25%, and 50% of the RD (DLA-1, -2, and -3) and reconstructed using FBP. We trained DLAs using the simulated CT images as input data and the RD CT images as ground truth. To test the DLA, the American College of Radiology CT phantom was used together with 18 patients who underwent abdominal LD CT. LD CT images of the phantom and patients were processed using FBP, ADMIRE, and DLAs (LD-FBP, LD-ADMIRE, and LD-DLA images, respectively). To compare the image quality, we measured the noise power spectrum and modulation transfer function (MTF) of phantom images. For patient data, we measured the mean image noise and performed qualitative image analysis. We evaluated the presence of additional artifacts in the LD-DLA images. Results LD-DLAs achieved lower noise levels than LD-FBP and LD-ADMIRE for both phantom and patient data (all p < 0.001). LD-DLAs trained with a lower radiation dose showed less image noise. However, the MTFs of the LD-DLAs were lower than those of LD-ADMIRE and LD-FBP (all p < 0.001) and decreased with decreasing training image dose. In the qualitative image analysis, the overall image quality of LD-DLAs was best for DLA-3 (50% simulated radiation dose) and not significantly different from LD-ADMIRE. There were no additional artifacts in LD-DLA images. Conclusion DLAs achieved less noise than FBP and ADMIRE in LD CT images, but did not maintain spatial resolution. The DLA trained with 50% simulated radiation dose showed the best overall image quality.
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Affiliation(s)
- Yoon Joo Shin
- Department of Radiology, Konkuk University Medical Center, Seoul, Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
| | - Jong Chul Ye
- Bio Imaging and Signal Processing Lab, Department of Bio and Brain Engineering, KAIST, Daejeon, Korea
| | - Eunhee Kang
- Bio Imaging and Signal Processing Lab, Department of Bio and Brain Engineering, KAIST, Daejeon, Korea
| | - Dong Yul Oh
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ji Hoon Park
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
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Magnetic Resonance Rectal Enema Versus Computed Tomographic Colonography in the Diagnosis of Rectosigmoid Endometriosis. J Comput Assist Tomogr 2020; 44:501-510. [PMID: 32558775 DOI: 10.1097/rct.0000000000001031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
OBJECTIVES Rectosigmoid involvement by endometriosis causes intestinal symptoms such as constipation, diarrhea, and dyschezia. A precise diagnosis about the presence, location, and extent of bowel implants is required to plan the most appropriate treatment. The aim of the study was to compare the accuracy of magnetic resonance with distension of the rectosigmoid (MR-e) with computed colonography (CTC) for diagnosing rectosigmoid endometriosis. METHODS This study was based on the retrospective analysis of a prospectively collected database of patients with suspicion of rectosigmoid endometriosis who underwent both MR-e and CTC, and subsequently were treated by laparoscopy. The findings of imaging techniques were compared with surgical and histological results. RESULTS Of 90 women included in the study, 44 (48.9%) had rectosigmoid nodules and underwent bowel surgery. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for the diagnosis of rectosigmoid endometriosis were 88.6%, 93.5%, 92.9%, 89.6%, and 91.1% for CTC, and 93.2%, 97.9%, 97.6%, 93.8%, and 95.6% for MR-e. There was no significant difference in the accuracy of both radiologic examinations for diagnosing rectosigmoid endometriosis (P = 0.344). However, MR-e was more accurate than CTC in estimating the largest diameter of the main rectosigmoid nodule (P < 0.001). The pain perceived by the patients was significantly lower during MR-e than during CTC (P < 0.001). CONCLUSIONS MR-e and CTC have similar diagnostic performance for the diagnosis of rectosigmoid involvement of endometriosis. However, MR-e is more accurate in the estimation of the largest diameter of main rectosigmoid nodule and more tolerated than CTC.
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Impact of ultra-low dose CT acquisition on semi-automated RECIST tool in the evaluation of malignant focal liver lesions. Diagn Interv Imaging 2020; 101:473-479. [DOI: 10.1016/j.diii.2020.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/05/2020] [Accepted: 05/06/2020] [Indexed: 12/21/2022]
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Peng C, Li B, Li M, Wang H, Zhao Z, Qiu B, Chen DZ. An irregular metal trace inpainting network for x‐ray CT metal artifact reduction. Med Phys 2020; 47:4087-4100. [DOI: 10.1002/mp.14295] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 05/10/2020] [Accepted: 05/11/2020] [Indexed: 01/08/2023] Open
Affiliation(s)
- Chengtao Peng
- Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei 230026 China
- Department of Computer Science and Engineering University of Notre Dame Notre Dame IN 46556 USA
| | - Bin Li
- Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei 230026 China
| | - Ming Li
- Medical Imaging Department Suzhou Institute of Biomedical Engineering and TechnologyChinese Academy of Science Suzhou 215163 China
| | - Hongxiao Wang
- Department of Computer Science and Engineering University of Notre Dame Notre Dame IN 46556 USA
| | - Zhuo Zhao
- Department of Computer Science and Engineering University of Notre Dame Notre Dame IN 46556 USA
| | - Bensheng Qiu
- Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei 230026 China
| | - Danny Z. Chen
- Department of Computer Science and Engineering University of Notre Dame Notre Dame IN 46556 USA
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Hsu JC, Nieves LM, Betzer O, Sadan T, Noël PB, Popovtzer R, Cormode DP. Nanoparticle contrast agents for X-ray imaging applications. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2020; 12:e1642. [PMID: 32441050 DOI: 10.1002/wnan.1642] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/13/2020] [Accepted: 04/14/2020] [Indexed: 12/12/2022]
Abstract
X-ray imaging is the most widely used diagnostic imaging method in modern medicine and several advanced forms of this technology have recently emerged. Iodinated molecules and barium sulfate suspensions are clinically approved X-ray contrast agents and are widely used. However, these existing contrast agents provide limited information, are suboptimal for new X-ray imaging techniques and are developing safety concerns. Thus, over the past 15 years, there has been a rapid growth in the development of nanoparticles as X-ray contrast agents. Nanoparticles have several desirable features such as high contrast payloads, the potential for long circulation times, and tunable physicochemical properties. Nanoparticles have also been used in a range of biomedical applications such as disease treatment, targeted imaging, and cell tracking. In this review, we discuss the principles behind X-ray contrast generation and introduce new types of X-ray imaging modalities, as well as potential elements and chemical compositions that are suitable for novel contrast agent development. We focus on the progress in nanoparticle X-ray contrast agents developed to be renally clearable, long circulating, theranostic, targeted, or for cell tracking. We feature agents that are used in conjunction with the newly developed multi-energy computed tomography and mammographic imaging technologies. Finally, we offer perspectives on current limitations and emerging research topics as well as expectations for the future development of the field. This article is categorized under: Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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Affiliation(s)
- Jessica C Hsu
- Department of Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, School of Engineering and Applied Science of the University of Pennsylvania, Pennsylvania, USA
| | - Lenitza M Nieves
- Department of Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biochemistry and Molecular Biophysics, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Oshra Betzer
- Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel
| | - Tamar Sadan
- Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel
| | - Peter B Noël
- Department of Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Rachela Popovtzer
- Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel
| | - David P Cormode
- Department of Radiology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, School of Engineering and Applied Science of the University of Pennsylvania, Pennsylvania, USA.,Department of Biochemistry and Molecular Biophysics, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Kambadakone A. Artificial Intelligence and CT Image Reconstruction: Potential of a New Era in Radiation Dose Reduction. J Am Coll Radiol 2020; 17:649-651. [DOI: 10.1016/j.jacr.2019.12.025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 12/24/2019] [Accepted: 12/27/2019] [Indexed: 12/22/2022]
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Karout L, El Asmar K, Naffaa L, Abi-Ghanem AS, El-Merhi F, Salman R, Saade C. Balancing act between quantitative and qualitative image quality between nonionic iodinated dimer and monomer at various vessel sizes during computed tomography: a phantom study. Biomed Phys Eng Express 2020; 6:035001. [PMID: 33438646 DOI: 10.1088/2057-1976/ab78dc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
PURPOSE Investigate the impact of nonionic dimer and monomer on iodine quantification in different vessel sizes when employing a vascular specific phantom and varying iodinated contrast media (ICM) concentrations during computed tomography (CT). MATERIALS AND METHODS We created a vascular specific phantom (30 cm) to simulate human blood vessel diameters (25 cylinders of different diameters: 10 × 9mm, 10 × 12mm and 5 × 21mm). The phantom was filled with two ICM separately: Group: Iohexol(monomer)350 mg ml-1 and B: Iodixanol(Dimer)320 mg ml-1. Cylinders of same size were filled with increasing ICM concentration(10%-100%) while large cylinders were filled in quartiles(25%-100%). Phantom was scanned with different tube potential (80-140kVp), current (50-400mAs), reconstruction method [filtered back projection (FBP), hybrid-based iterative reconstruction (HBIR) and model-based iterative reconstruction (MBIR)] for each ICM. Chi-square was employed to compare mean opacification, contrast/noise ratio (CNR) and noise. Qualitative analysis was assessed by Visual grading characteristic (VGC) and Cohens-kappa analyses. RESULTS At 80 and140kVp significant difference in opacification between Group A (2054 ± 1040HU and 1696 ± 1027HU) and B (2169 ± 1105HU and 1568 ± 1034HU) was demonstrated (p < 0.001). However, at 100 and 120kVp no difference was noted (p > 0.05). When comparing image noise, it was higher in Group A compared to B (p < 0.05). CNR was higher in Group B (119.99 ± 126.10HU) than A (107.09 ± 102.56HU) (p < 0.0001). VGC: Group A outperformed B in image opacification in all vessel sizes and ICM concentrations except at medium vessels with concentration group 2(0.4-0.6 mg ml-1). Cohens'-kappa: agreement in opacification between each ICM group and iodine concentration 1(0-0.3 mg ml-1): κ = 0.253 and 0.014 respectively, concentration 2(0.4-0.6 mg ml-1):κ = -0.017 and -0.005 respectively and concentration 3(0.7-1 mg ml-1):κ = 0.031 and 0.115 respectively. CONCLUSION Nonionic dimer (Iodixanol) surpasses monomer (Iohexol) in quantitative image quality assessment by having lower image noise and higher CNR during CT.
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Affiliation(s)
- Lina Karout
- Diagnostic Radiology Department, American University of Beirut Medical Center, Beirut, American University of Beirut Medical Center, Beirut, Lebanon. P O Box: 11-0236 Riad El-Solh, Beirut, 1107 2020, Lebanon
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Kawashima H, Ichikawa K, Takata T, Mitsui W. Algorithm-based artifact reduction in patients with arms-down positioning in computed tomography. Phys Med 2020; 69:61-69. [DOI: 10.1016/j.ejmp.2019.11.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 11/06/2019] [Accepted: 11/18/2019] [Indexed: 11/25/2022] Open
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You C, Li G, Zhang Y, Zhang X, Shan H, Li M, Ju S, Zhao Z, Zhang Z, Cong W, Vannier MW, Saha PK, Hoffman EA, Wang G. CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:188-203. [PMID: 31217097 PMCID: PMC11662229 DOI: 10.1109/tmi.2019.2922960] [Citation(s) in RCA: 190] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel 1×1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.
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Kanii Y, Ichikawa Y, Nakayama R, Nagata M, Ishida M, Kitagawa K, Murashima S, Sakuma H. Usefulness of dictionary learning-based processing for improving image quality of sub-millisievert low-dose chest CT: initial experience. Jpn J Radiol 2019; 38:215-221. [PMID: 31863329 DOI: 10.1007/s11604-019-00912-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/14/2019] [Indexed: 11/29/2022]
Abstract
PURPOSE To develop a dictionary learning (DL)-based processing technique for improving the image quality of sub-millisievert chest computed tomography (CT). MATERIALS AND METHODS Standard-dose and sub-millisievert chest CT were acquired in 12 patients. Dictionaries including standard- and low-dose image patches were generated from the CT datasets. For each patient, DL-based processing was performed for low-dose CT using the dictionaries generated from the remaining 11 patients. This procedure was repeated for all 12 patients. Image quality of normal thoracic structures on the processed sub-millisievert CT images was assessed with a 5-point scale (5 = excellent, 1 = very poor). Lung lesion conspicuity was also assessed on a 5-point scale. RESULTS Image noise on sub-millisievert CT was significantly decreased with DL-based image processing (48.5 ± 13.7 HU vs 20.4 ± 7.9 HU, p = 0.0005). Image quality of lung structures was significantly improved with DL-based method (middle level of lung, 2.25 ± 0.75 vs 2.92 ± 0.79, p = 0.0078). Lung lesion conspicuity was also significantly improved with DL-based technique (solid nodules, 3.4 ± 0.6 vs 2.7 ± 0.6, p = 0.0273). CONCLUSION Image quality and lesion conspicuity on sub-millisievert chest CT images may be improved by DL-based post-processing.
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Affiliation(s)
- Yoshinori Kanii
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Yasutaka Ichikawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
| | - Ryohei Nakayama
- Department of Electronic and Computer Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577, Japan
| | - Motonori Nagata
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Masaki Ishida
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Kakuya Kitagawa
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
| | - Shuichi Murashima
- Department of Radiology, Matsusaka Chuo General Hospital, 102 Kobou, Kawai, Matsusaka, Mie, 515-8566, Japan
| | - Hajime Sakuma
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan
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Mileto A, Guimaraes LS, McCollough CH, Fletcher JG, Yu L. State of the Art in Abdominal CT: The Limits of Iterative Reconstruction Algorithms. Radiology 2019; 293:491-503. [DOI: 10.1148/radiol.2019191422] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Achille Mileto
- From the Department of Radiology, University of Washington School of Medicine, Seattle, Wash (A.M.); Joint Department of Medical Imaging, Sinai Health System, University of Toronto, Toronto, Ontario, Canada (L.S.G.); and Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (C.H.M., J.G.F., L.Y.)
| | - Luis S. Guimaraes
- From the Department of Radiology, University of Washington School of Medicine, Seattle, Wash (A.M.); Joint Department of Medical Imaging, Sinai Health System, University of Toronto, Toronto, Ontario, Canada (L.S.G.); and Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (C.H.M., J.G.F., L.Y.)
| | - Cynthia H. McCollough
- From the Department of Radiology, University of Washington School of Medicine, Seattle, Wash (A.M.); Joint Department of Medical Imaging, Sinai Health System, University of Toronto, Toronto, Ontario, Canada (L.S.G.); and Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (C.H.M., J.G.F., L.Y.)
| | - Joel G. Fletcher
- From the Department of Radiology, University of Washington School of Medicine, Seattle, Wash (A.M.); Joint Department of Medical Imaging, Sinai Health System, University of Toronto, Toronto, Ontario, Canada (L.S.G.); and Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (C.H.M., J.G.F., L.Y.)
| | - Lifeng Yu
- From the Department of Radiology, University of Washington School of Medicine, Seattle, Wash (A.M.); Joint Department of Medical Imaging, Sinai Health System, University of Toronto, Toronto, Ontario, Canada (L.S.G.); and Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (C.H.M., J.G.F., L.Y.)
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Noorbakhsh A, Aganovic L, Vahdat N, Fazeli S, Chung R, Cassidy F. What a difference a delay makes! CT urogram: a pictorial essay. Abdom Radiol (NY) 2019; 44:3919-3934. [PMID: 31214728 PMCID: PMC8882435 DOI: 10.1007/s00261-019-02086-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE The aim of this pictorial essay is to demonstrate several cases where the diagnosis would have been difficult or impossible without the excretory phase image of CT urography. METHODS A brief discussion of CT urography technique and dose reduction is followed by several cases illustrating the utility of CT urography. RESULTS CT urography has become the primary imaging modality for evaluation of hematuria, as well as in the staging and surveillance of urinary tract malignancies. CT urography includes a non-contrast phase and contrast-enhanced nephrographic and excretory (delayed) phases. While the three phases add to the diagnostic ability of CT urography, it also adds potential patient radiation dose. Several techniques including automatic exposure control, iterative reconstruction algorithms, higher noise tolerance, and split-bolus have been successfully used to mitigate dose. The excretory phase is timed such that the excreted contrast opacifies the urinary collecting system and allows for greater detection of filling defects or other abnormalities. Sixteen cases illustrating the utility of excretory phase imaging are reviewed. CONCLUSIONS Excretory phase imaging of CT urography can be an essential tool for detecting and appropriately characterizing urinary tract malignancies, renal papillary and medullary abnormalities, CT radiolucent stones, congenital abnormalities, certain chronic inflammatory conditions, and perinephric collections.
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Affiliation(s)
- Abraham Noorbakhsh
- Department of Radiology, University of California, San Diego Health, San Diego, USA
| | - Lejla Aganovic
- Department of Radiology, University of California, San Diego Health, San Diego, USA
- Department of Radiology, Veterans Affairs San Diego Healthcare, San Diego, CA, USA
| | - Noushin Vahdat
- Department of Radiology, University of California, San Diego Health, San Diego, USA
- Department of Radiology, Veterans Affairs San Diego Healthcare, San Diego, CA, USA
| | - Soudabeh Fazeli
- Department of Radiology, University of California, San Diego Health, San Diego, USA
| | - Romy Chung
- Department of Radiology, University of California, San Diego Health, San Diego, USA
| | - Fiona Cassidy
- Department of Radiology, University of California, San Diego Health, San Diego, USA.
- Department of Radiology, Veterans Affairs San Diego Healthcare, San Diego, CA, USA.
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Miller C, Mittelstaedt D, Black N, Klahr P, Nejad-Davarani S, Schulz H, Goshen L, Han X, Ghanem AI, Morris ED, Glide-Hurst C. Impact of CT reconstruction algorithm on auto-segmentation performance. J Appl Clin Med Phys 2019; 20:95-103. [PMID: 31538718 PMCID: PMC6753741 DOI: 10.1002/acm2.12710] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 06/28/2019] [Accepted: 07/20/2019] [Indexed: 11/21/2022] Open
Abstract
Model‐based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity‐based tasks such as auto‐segmentation. This work evaluates the sensitivity of an auto‐contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP). Images were created from raw projection data for 11 prostate cancer cases using FBP and nine different MBIR reconstructions (3 protocols/3 noise reduction levels) yielding 10 reconstructions/patient. Five bony structures, bladder, rectum, prostate, and seminal vesicles (SVs) were segmented using an auto‐segmentation pipeline that renders 3D binary masks for analysis. Performance was evaluated for volume percent difference (VPD) and Dice similarity coefficient (DSC), using FBP as the gold standard. Nonparametric Friedman tests plus post hoc all pairwise comparisons were employed to test for significant differences (P < 0.05) for soft tissue organs and protocol/level combinations. A physician performed qualitative grading of 396 MBIR contours across the prostate, bladder, SVs, and rectum in comparison to FBP using a six‐point scale. MBIR contours agreed with FBP for bony anatomy (DSC ≥ 0.98), bladder (DSC ≥ 0.94, VPD < 8.5%), and prostate (DSC = 0.94 ± 0.03, VPD = 4.50 ± 4.77% (range: 0.07–26.39%). Increased variability was observed for rectum (VPD = 7.50 ± 7.56% and DSC = 0.90 ± 0.08) and SVs (VPD and DSC of 8.23 ± 9.86% range (0.00–35.80%) and 0.87 ± 0.11, respectively). Over the all protocol/level comparisons, a significant difference was observed for the prostate VPD between BSPL1 and BSTL2 (adjusted P‐value = 0.039). Nevertheless, 300 of 396 (75.8%) of the four soft tissue structures using MBIR were graded as equivalent or better than FBP, suggesting that MBIR offered potential improvements in auto‐segmentation performance when compared to FBP. Future work may involve tuning organ‐specific MBIR parameters to further improve auto‐segmentation performance. Running title: Impact of CT Reconstruction Algorithm on Auto‐segmentation Performance.
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Affiliation(s)
- Claudia Miller
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA.,Wayne State University, Detroit, MI, USA
| | - Daniel Mittelstaedt
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA
| | - Noel Black
- Department of CT Imaging Physics, Philips Healthcare, Cleveland, OH, USA
| | - Paul Klahr
- Department of CT Imaging Physics, Philips Healthcare, Cleveland, OH, USA
| | | | | | - Liran Goshen
- Department of CT Imaging Physics, Philips Healthcare, Cleveland, OH, USA
| | - Xiaoxia Han
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ahmed I Ghanem
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA.,Clinical Oncology Department, Alexandria University, Alexandria, Egypt
| | - Eric D Morris
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA.,Wayne State University, Detroit, MI, USA
| | - Carri Glide-Hurst
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA.,Wayne State University, Detroit, MI, USA
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45
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Sun J, Zhang Q, Hu D, Shen Y, Yang H, Chen C, Zhou Z, Peng Y. Feasibility study of using one-tenth mSv radiation dose in young children chest CT with 80 kVp and model-based iterative reconstruction. Sci Rep 2019; 9:12481. [PMID: 31462667 PMCID: PMC6713735 DOI: 10.1038/s41598-019-48946-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 08/16/2019] [Indexed: 01/24/2023] Open
Abstract
CT has become a routine imaging modality based on its excellent ability of displaying lung structures and diseases. But, how to reduce radiation dose of routine CT examination is a concern for radiologists. Our study aimed to evaluate the feasibility of using 80kVp and a model-based iterative reconstruction (MBIR) algorithm to achieve one-tenth mSv dose chest CT in infants and young children. Thirty-two cases (study group, average age 1.71 ± 1.01 years) underwent non-contrast chest CT examination at low dose with 80 kV, 4mAs and was reconstructed with MBIR (LD-MBIR) and the standard adaptive statistical iterative reconstruction (ASIR) algorithm (LD-ASIR); another group (control group) of 32 children underwent routine-dose chest CT with 100 kV and was reconstructed with ASIR only (RD-ASIR). The subjective and objective image quality of the three groups were measured and statistically compared. The radiation dose for the low dose scan was 0.09 ± 0.02 mSv, 6% of the routine dose. All LD-MBIR images were diagnostically acceptable. Compared with the RD-ASIR images, the LD-MBIR images were similar in noise in the left ventricle, muscles, lung field, on-par in displaying large airways, lung lucency and mediastinum, but were inferior in displaying lung marking, small airways and mediastinum. Thus, MBIR images with low dose in pediatric chest CT can be used in the diagnosis for lung field and air way disorders in infants and young children.
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Affiliation(s)
- Jihang Sun
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China
| | - Qifeng Zhang
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China
| | - Di Hu
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China
| | - Yun Shen
- Department of Radiology, Tokyo Women's Medical University &Medical Center East, Tokyo, 116-8567, Japan
| | - Haiming Yang
- Respiratory Department, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China
| | - Chenghao Chen
- Department of Thoracic surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China
| | - Zuofu Zhou
- Department of radiology, Fujian Provincial Maternity and Children's Hospital, affiliated hospital of Fujian Medical University, No.18 Daoshan Road, Gulou District, Fujian, 350000, China
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing, 100045, China.
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46
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Gao Y, Liang Z, Moore WH, Zhang H, Pomeroy MJ, Ferretti JA, Bilfinger TV, Ma J, Lu H. A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1981-1992. [PMID: 30605098 PMCID: PMC6610633 DOI: 10.1109/tmi.2018.2890788] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient's previous full-dose CT (FdCT) scans as prior knowledge. It showed advantages in clinical applications. This paper aims to remove the constraint of using previous data of the same patient. We investigated the feasibility of extracting the tissue-specific MRF textures from an FdCT database to reconstruct a LdCT image of another patient. This feasibility study was carried out by experiments designed as follows. We constructed a tissue-specific MRF-texture database from 3990 FdCT scan slices of 133 patients who were scheduled for lung nodule biopsy. Each patient had one FdCT scan (120 kVp/100 mAs) and one LdCT scan (120 kVp/20 mAs) prior to biopsy procedure. When reconstructing the LdCT image of one patient among the 133 patients, we ranked the closeness of the MRF-textures from the other 132 patients saved in the database and used them as the a prior knowledge. Then, we evaluated the reconstructed image quality using Haralick texture measures. For any patient within our database, we found more than eighteen patients' FdCT MRF texures can be used without noticeably changing the Haralick texture measures on the lung nodules (to be biopsied). These experimental outcomes indicate it is promising that a sizable FdCT texture database could be used to enhance Bayesian reconstructions of any incoming LdCT scans.
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Affiliation(s)
- Yongfeng Gao
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY 11974 USA
| | - Zhengrong Liang
- Departments of Radiology, Electrical and Computer Engineering, Computer Science and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA ()
| | - William H. Moore
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY 11794, USA, and now is with the Department of Radiology, New York University, New York, NY 10016, USA
| | - Hao Zhang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94035, USA
| | - Marc J. Pomeroy
- Departments of Radiology and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - John A. Ferretti
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - Thomas V. Bilfinger
- Department of Surgery, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Hongbing Lu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an 710032, China
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47
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Lin S, Lin M, Lau KK. Image quality comparison between model-based iterative reconstruction and adaptive statistical iterative reconstruction chest computed tomography in cystic fibrosis patients. J Med Imaging Radiat Oncol 2019; 63:602-609. [PMID: 31090256 DOI: 10.1111/1754-9485.12895] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 04/10/2019] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Cystic fibrosis (CF) predominantly affects young adults. Accurate radiological assessment of pulmonary disease is vital for predicting exacerbations, one of the leading causes of morbidity and mortality. We evaluated the image quality of model-based iterative reconstruction (MBIR) ultra-low-dose CT chest (ULD-CT) in CF evaluation. METHODS We compared ULD-CT with standard adaptive statistical iterative reconstruction (ASIR) low-dose CT (LD-CT). Subjective assessment of contrast and noise were performed for each study. Background noise, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated and compared between the CT studies. Conspicuity of major structures was assessed. These aspects of image quality were compared to determine whether ULD-CT was superior to LD-CT in assessment of CF. RESULTS The ULD-CT achieved median effective dose of 0.073 mSv, comparable to one standard chest radiograph and significantly lower than the median LD-CT dose of 1.22 mSv. ULD-CT had lower subjective contrast and higher subjective noise when compared to LD-CT. Objectively measured background noise was lower in ULD-CT (16.33 HU vs 38.53 HU, P < 0.0001) compared to LD-CT. ULD-CT had higher median CNR (52.65 vs 22.09, P < 0.0001) and SNR in lung (9.08 vs 7.29, P = 0.002) compared to LD-CT. ULD-CT was equal to LD-CT in identification of trachea, bronchi, pleural and pericardium. Interobserver reliability showed agreement of 80-92%. CONCLUSIONS The image quality of ULD-CT is similar to LD-CT, at 1/16th the dose. MBIR constructed ULD-CT is an effective imaging modality for CF surveillance, with potential applications in other disease settings.
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Affiliation(s)
- Sandra Lin
- Austin Health, Melbourne, Victoria, Australia
| | - Monica Lin
- Department of Diagnostic Imaging, Monash Health, Melbourne, Victoria, Australia
| | - Kenneth K Lau
- Department of Diagnostic Imaging, Monash Health, Melbourne, Victoria, Australia.,Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
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48
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Erdal BS, Prevedello LM, Qian S, Demirer M, Little K, Ryu J, O'Donnell T, White RD. Radiology and Enterprise Medical Imaging Extensions (REMIX). J Digit Imaging 2019; 31:91-106. [PMID: 28840365 PMCID: PMC5788816 DOI: 10.1007/s10278-017-0010-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of “big imaging data,” as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence. These capabilities, along with documentation and guidance, explaining how to interact with a commercial system (e.g., PACS, EHR, commercial database) that currently exists in clinical environments, are to be made freely available.
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Affiliation(s)
- Barbaros S Erdal
- Radiology Department, The Ohio State University Wexner Medical Center, 395 W 12th Ave, Columbus, OH, 43210, USA.
| | - Luciano M Prevedello
- Radiology Department, The Ohio State University Wexner Medical Center, 395 W 12th Ave, Columbus, OH, 43210, USA
| | - Songyue Qian
- Radiology Department, The Ohio State University Wexner Medical Center, 395 W 12th Ave, Columbus, OH, 43210, USA
| | - Mutlu Demirer
- Radiology Department, The Ohio State University Wexner Medical Center, 395 W 12th Ave, Columbus, OH, 43210, USA
| | - Kevin Little
- Radiology Department, The Ohio State University Wexner Medical Center, 395 W 12th Ave, Columbus, OH, 43210, USA
| | - John Ryu
- Radiology Department, The Ohio State University Wexner Medical Center, 395 W 12th Ave, Columbus, OH, 43210, USA
| | - Thomas O'Donnell
- Siemens Medical Solutions USA, Inc, 40 Liberty Boulevard, Malvern, PA, 19355, USA
| | - Richard D White
- Radiology Department, The Ohio State University Wexner Medical Center, 395 W 12th Ave, Columbus, OH, 43210, USA
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Mackin D, Ger R, Gay S, Dodge C, Zhang L, Yang J, Jones AK, Court L. Matching and Homogenizing Convolution Kernels for Quantitative Studies in Computed Tomography. Invest Radiol 2019; 54:288-295. [PMID: 30570504 PMCID: PMC6449212 DOI: 10.1097/rli.0000000000000540] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The sharpness of the kernels used for image reconstruction in computed tomography affects the values of the quantitative image features. We sought to identify the kernels that produce similar feature values to enable a more effective comparison of images produced using scanners from different manufactures. We also investigated a new image filter designed to change the kernel-related component of the frequency spectrum of a postreconstruction image from that of the initial kernel to that of a preferred kernel. A radiomics texture phantom was imaged using scanners from GE, Philips, Siemens, and Toshiba. Images were reconstructed multiple times, varying the kernel from smooth to sharp. The phantom comprised 10 cartridges of various textures. A semiautomated method was used to produce 8 × 2 × 2 cm regions of interest for each cartridge and for all scans. For each region of interest, 38 radiomics features from the categories intensity direct (n = 12), gray-level co-occurrence matrix (n = 21), and neighborhood gray-tone difference matrix (n = 5) were extracted. We then calculated the fractional differences of the features from those of the baseline kernel (GE Standard). To gauge the importance of the differences, we scaled them by the coefficient of variation of the same feature from a cohort of patients with non-small cell lung cancer. The noise power spectra for each kernel were estimated from the phantom's solid acrylic cartridge, and kernel-homogenization filters were developed from these estimates. The Philips C, Siemens B30f, and Toshiba FC24 kernels produced feature values most similar to GE Standard. The kernel homogenization filters reduced the median differences from baseline to less than 1 coefficient of variation in the patient population for all of the GE, Philips, and Siemens kernels except for GE Edge and Toshiba kernels. For prospective computed tomographic radiomics studies, the scanning protocol should specify kernels that have been shown to produce similar feature values. For retrospective studies, kernel homogenization filters can be designed and applied to reduce the kernel-related differences in the feature values.
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Affiliation(s)
- Dennis Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Rachel Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Skylar Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Cristina Dodge
- Department of Diagnostic Imaging, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - A. Kyle Jones
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Radiation Oncology Department, Houston Methodist Hospital, Houston, TX 77030, USA
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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50
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Ippolito D, De Vito A, Franzesi CT, Riva L, Pecorelli A, Corso R, Crespi A, Sironi S. Evaluation of image quality and radiation dose saving comparing knowledge model-based iterative reconstruction on 80-kV CT pulmonary angiography (CTPA) with hybrid iterative reconstruction on 100-kV CT. Emerg Radiol 2019; 26:145-153. [PMID: 30415416 DOI: 10.1007/s10140-018-1653-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 10/25/2018] [Indexed: 02/08/2023]
Abstract
OBJECTIVES To evaluate dose reduction and image quality of 80-kV CT pulmonary angiography (CTPA) reconstructed with knowledge model-based iterative reconstruction (IMR), and compared with 100-kV CTPA with hybrid iterative reconstruction (iDose4). MATERIALS AND METHODS One hundred and fifty-one patients were prospectively investigated for pulmonary embolism; a study group of 76 patients underwent low-kV setting (80 kV, automated mAs) CTPA study, while a control group of 75 patients underwent standard CTPA protocol (100 kV; automated mAs); all patients were examined on 256 MDCT scanner (Philips iCTelite). Study group images were reconstructed using IMR while the control group ones with iDose4. CTDIvol, DLP, and ED were evaluated. Region of interests placed in the main pulmonary vessels evaluated vascular enhancement (HU); signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. RESULTS Compared to iDose4-CTPA, low-kV IMR-CTPA presented lower CTDIvol (6.41 ± 0.84 vs 9.68 ± 3.5 mGy) and DLP (248.24 ± 3.2 vs 352.4 ± 3.59 mGy × cm), with ED of 3.48 ± 1.2 vs 4.93 ± 1.8 mSv. Moreover, IMR-CTPA showed higher values of attenuation (670.91 ± 9.09 HU vs 292.61 ± 15.5 HU) and a significantly higher SNR (p < 0.0001) and CNR (p < 0.0001).The subjective image quality of low-kV IMR-CTPA was also higher compared with iDose4-CTPA (p < 0.0001). CONCLUSIONS Low-dose CTPA (80 kV and automated mAs modulation) reconstructed with IMR represents a feasible protocol for the diagnosis of pulmonary embolism in the emergency setting, achieving high image quality with low noise, and a significant dose reduction within adequate reconstruction times(≤ 120 s).
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Affiliation(s)
- Davide Ippolito
- Department of Diagnostic Radiology, "San Gerardo" Hospital, Via Pergolesi 33, 20900, Monza, MB, Italy.
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900, Monza, MB, Italy.
| | - Andrea De Vito
- Department of Diagnostic Radiology, "San Gerardo" Hospital, Via Pergolesi 33, 20900, Monza, MB, Italy
- School of Medicine, University of Milano-Bicocca, Milan, MI, Italy
| | - Cammillo Talei Franzesi
- Department of Diagnostic Radiology, "San Gerardo" Hospital, Via Pergolesi 33, 20900, Monza, MB, Italy
- School of Medicine, University of Milano-Bicocca, Milan, MI, Italy
| | - Luca Riva
- Department of Diagnostic Radiology, "San Gerardo" Hospital, Via Pergolesi 33, 20900, Monza, MB, Italy
- School of Medicine, University of Milano-Bicocca, Milan, MI, Italy
| | - Anna Pecorelli
- Department of Diagnostic Radiology, "San Gerardo" Hospital, Via Pergolesi 33, 20900, Monza, MB, Italy
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900, Monza, MB, Italy
| | - Rocco Corso
- Department of Diagnostic Radiology, "San Gerardo" Hospital, Via Pergolesi 33, 20900, Monza, MB, Italy
| | - Andrea Crespi
- School of Medicine, University of Milano-Bicocca, Milan, MI, Italy
- Department of Medical Physics, "San Gerardo" Hospital, Monza, MB, Italy
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900, Monza, MB, Italy
- Department of Diagnostic Radiology, H Papa Giovanni XIII, Piazza OMS 1, 24127, Bergamo, BG, Italy
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