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Kniep I, Mieling R, Gerling M, Schlaefer A, Heinemann A, Ondruschka B. Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context. J Imaging 2023; 9:170. [PMID: 37754934 PMCID: PMC10532172 DOI: 10.3390/jimaging9090170] [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: 06/30/2023] [Revised: 08/17/2023] [Accepted: 08/21/2023] [Indexed: 09/28/2023] Open
Abstract
Computed tomography (CT) is a widely used examination technique that usually requires a compromise between image quality and radiation exposure. Reconstruction algorithms aim to reduce radiation exposure while maintaining comparable image quality. Recently, unsupervised deep learning methods have been proposed for this purpose. In this study, a promising sparse-view reconstruction method (posterior temperature optimized Bayesian inverse model; POTOBIM) is tested for its clinical applicability. For this study, 17 whole-body CTs of deceased were performed. In addition to POTOBIM, reconstruction was performed using filtered back projection (FBP). An evaluation was conducted by simulating sinograms and comparing the reconstruction with the original CT slice for each case. A quantitative analysis was performed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The quality was assessed visually using a modified Ludewig's scale. In the qualitative evaluation, POTOBIM was rated worse than the reference images in most cases. A partially equivalent image quality could only be achieved with 80 projections per rotation. Quantitatively, POTOBIM does not seem to benefit from more than 60 projections. Although deep learning methods seem suitable to produce better image quality, the investigated algorithm (POTOBIM) is not yet suitable for clinical routine.
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Affiliation(s)
- Inga Kniep
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany; (M.G.); (A.H.); (B.O.)
| | - Robin Mieling
- Institute for Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073 Hamburg, Germany;
| | - Moritz Gerling
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany; (M.G.); (A.H.); (B.O.)
| | - Alexander Schlaefer
- Institute for Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073 Hamburg, Germany;
| | - Axel Heinemann
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany; (M.G.); (A.H.); (B.O.)
| | - Benjamin Ondruschka
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany; (M.G.); (A.H.); (B.O.)
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Otgonbaatar C, Jeon PH, Ryu JK, Shim H, Jeon SH, Ko SM, Kim H. Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques. Acta Radiol 2023; 64:2393-2400. [PMID: 37211615 DOI: 10.1177/02841851231174463] [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] [Indexed: 05/23/2023]
Abstract
BACKGROUND The reference protocol for the quantification of coronary artery calcium (CAC) should be updated to meet the standards of modern imaging techniques. PURPOSE To assess the influence of filtered-back projection (FBP), hybrid iterative reconstruction (IR), and three levels of deep learning reconstruction (DLR) on CAC quantification on both in vitro and in vivo studies. MATERIAL AND METHODS In vitro study was performed with a multipurpose anthropomorphic chest phantom and small pieces of bones. The real volume of each piece was measured using the water displacement method. In the in vivo study, 100 patients (84 men; mean age = 71.2 ± 8.7 years) underwent CAC scoring with a tube voltage of 120 kVp and image thickness of 3 mm. The image reconstruction was done with FBP, hybrid IR, and three levels of DLR including mild (DLRmild), standard (DLRstd), and strong (DLRstr). RESULTS In the in vitro study, the calcium volume was equivalent (P = 0.949) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. In the in vivo study, the image noise was significantly lower in images that used DLRstr-based reconstruction, when compared images other reconstructions (P < 0.001). There were no significant differences in the calcium volume (P = 0.987) and Agatston score (P = 0.991) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. The highest overall agreement of Agatston scores was found in the DLR groups (98%) and hybrid IR (95%) when compared to standard FBP reconstruction. CONCLUSION The DLRstr presented the lowest bias of agreement in the Agatston scores and is recommended for the accurate quantification of CAC.
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Affiliation(s)
| | - Pil-Hyun Jeon
- Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju, Republic of Korea
| | - Jae-Kyun Ryu
- Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea
| | - Hackjoon Shim
- Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea
- ConnectAI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sang-Hyun Jeon
- Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju, Republic of Korea
| | - Sung Min Ko
- Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju, Republic of Korea
| | - Hyunjung Kim
- Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju, Republic of Korea
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Zhong J, Shen H, Chen Y, Xia Y, Shi X, Lu W, Li J, Xing Y, Hu Y, Ge X, Ding D, Jiang Z, Yao W. Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT. J Digit Imaging 2023; 36:1390-1407. [PMID: 37071291 PMCID: PMC10406981 DOI: 10.1007/s10278-023-00806-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 04/19/2023] Open
Abstract
This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, 215028 China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ UK
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203 China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176 China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Zhenming Jiang
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336 China
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Hirairi T, Ichikawa K, Urikura A, Kawashima H, Tabata T, Matsunami T. Improvement of diagnostic performance of hyperacute ischemic stroke in head CT using an image-based noise reduction technique with non-black-boxed process. Phys Med 2023; 112:102646. [PMID: 37549457 DOI: 10.1016/j.ejmp.2023.102646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/05/2023] [Accepted: 07/28/2023] [Indexed: 08/09/2023] Open
Abstract
PURPOSE This study aims to investigate whether an image-based noise reduction (INR) technique with a conventional rule-based algorithm involving no black-boxed processes can outperform an existing hybrid-type iterative reconstruction (HIR) technique, when applied to brain CT images for diagnosis of early CT signs, which generally exhibit low-contrast lesions that are difficult to detect. METHODS The subjects comprised 27 patients having infarctions within 4.5 h of onset and 27 patients with no change in brain parenchyma. Images with thicknesses of 5 mm and 0.625 mm were reconstructed by HIR. Images with a thickness of 0.625 mm reconstructed by filter back projection (FBP) were processed by INR. The contrast-to-noise ratios (CNRs) were calculated between gray and white matters; lentiform nucleus and internal capsule; infarcted and non-infarcted areas. Two radiologists subjectively evaluated the presence of hyperdense artery signs (HASs) and infarctions and visually scored three properties regarding image quality (0.625-mm HIR images were excluded because of their notably worse noise appearances). RESULTS The CNRs of INR were significantly better than those of HIR with P < 0.001 for all the indicators. INR yielded significantly higher areas under the curve for both infarction and HAS detections than HIR (P < 0.001). Also, INR significantly improved the visual scores of all the three indicators. CONCLUSION The INR incorporating a simple and reproducible algorithm was more effective than HIR in detecting early CT signs and can be potentially applied to CT images from a large variety of CT systems.
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Affiliation(s)
- Tetsuya Hirairi
- Department of Radiological Technology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| | - Katsuhiro Ichikawa
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Atsushi Urikura
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuuouku, Tokyo, 104-0045, Japan.
| | - Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan.
| | - Takasumi Tabata
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| | - Tamaki Matsunami
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
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Takai Y, Noda Y, Asano M, Kawai N, Kaga T, Tsuchida Y, Miyoshi T, Hyodo F, Kato H, Matsuo M. Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction. Eur J Radiol 2023; 165:110960. [PMID: 37423016 DOI: 10.1016/j.ejrad.2023.110960] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/19/2023] [Accepted: 07/03/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE To evaluate the image quality and visibility of pancreatic ductal adenocarcinoma (PDAC) in 80-kVp pancreatic CT protocol and compare them between hybrid-iterative reconstruction (IR) and deep-learning image reconstruction (DLIR) algorithms. METHOD A total of 56 patients who underwent 80-kVp pancreatic protocol CT for pancreatic disease evaluation from January 2022 to July 2022 were included in this retrospective study. Among them, 20 PDACs were observed. The CT raw data were reconstructed using 40% adaptive statistical IR-Veo (hybrid-IR group) and DLIR at medium- and high-strength levels (DLIR-M and DLIR-H groups, respectively). The CT attenuation of the abdominal aorta, pancreas, and PDAC (if present) at the pancreatic phase and those of the portal vein and liver at the portal venous phase; background noise; signal-to-noise ratio (SNR) of these anatomical structures; and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. The confidence scores for the image noise, overall image quality, and visibility of PDAC were qualitatively assigned using a five-point scale. Quantitative and qualitative parameters were compared among the three groups using Friedman test. RESULTS The CT attenuation of all anatomical structures were comparable among the three groups (P = .26-.86), except that of the pancreas (P = .001). Background noise was lower (P <.001) and SNRs (P <.001) and tumor-to-pancreas CNR (P <.001) were higher in the DLIR-H group than those in the other two groups. The image noise, overall image quality, and visibility of PDAC were better in the DLIR-H group than in the other two groups (P <.001-.003). CONCLUSION In 80-kVp pancreatic CT protocol, DLIR at a high-strength level improved image quality and visibility of PDAC.
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Affiliation(s)
- Yukiko Takai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Yoshifumi Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Masashi Asano
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Nobuyuki Kawai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Tetsuro Kaga
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Yuki Tsuchida
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Toshiharu Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Fuminori Hyodo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan; Institute for Advanced Study, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Hiroki Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Masayuki Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
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Liao S, Mo Z, Zeng M, Wu J, Gu Y, Li G, Quan G, Lv Y, Liu L, Yang C, Wang X, Huang X, Zhang Y, Cao W, Dong Y, Wei Y, Zhou Q, Xiao Y, Zhan Y, Zhou XS, Shi F, Shen D. Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction. Cell Rep Med 2023; 4:101119. [PMID: 37467726 PMCID: PMC10394257 DOI: 10.1016/j.xcrm.2023.101119] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 05/16/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.
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Affiliation(s)
- Shu Liao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Mengsu Zeng
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yuning Gu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Guobin Li
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Guotao Quan
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Yang Lv
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Chun Yang
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xinglie Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Xiaoqian Huang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yang Zhang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Wenjing Cao
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Yun Dong
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yongqin Xiao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Yiqiang Zhan
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China.
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Shanghai Clinical Research and Trial Center, Shanghai 200122, China.
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Bohang SAM, Sohaimi N. An Overview on the Alignment of Radiation Protection in Computed Tomography with Maqasid al-Shari'ah in the Context of al-Dharuriyat. Malays J Med Sci 2023; 30:60-72. [PMID: 37425388 PMCID: PMC10325131 DOI: 10.21315/mjms2023.30.3.5] [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: 07/26/2021] [Accepted: 01/08/2022] [Indexed: 07/11/2023] Open
Abstract
The increasing utilisation of computed tomography (CT) in the medical field has raised a greater concern regarding the radiation-induced health effects as CT imposes high radiation risks on the exposed individual. Adherence to radiation protection measures in CT as endorsed by regulatory bodies; justification, optimisation and dose limit, is essential to minimise radiation risks. Islam values every human being and Maqasid al-Shari'ah helps to protect human beings through its sacred principles which aim to fulfil human beings' benefits (maslahah) and prevent mischief (mafsadah). Alignment of the concept of radiation protection in CT within the framework of al-Dharuriyat; protection of faith or religion (din), protection of life (nafs), protection of lineage (nasl), protection of intellect ('aql) and protection of property (mal) is essential. This strengthens the concept and practices of radiation protection in CT among radiology personnel, particularly Muslim radiographers. The alignment provides supplementary knowledge towards the integration of knowledge fields between Islamic worldview and radiation protection in medical imaging, particularly in CT. This paper is hoped to set a benchmark for future studies on the integration of knowledge between the Islamic worldview and radiation protection in medical imaging in terms of other classifications of Maqasid al-Shari'ah; al-Hajiyat and al-Tahsiniyat.
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Affiliation(s)
- Siti Aisyah Munirah Bohang
- Department of Diagnostic Imaging and Radiotherapy, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Pahang, Malaysia
| | - Norhanna Sohaimi
- Department of Diagnostic Imaging and Radiotherapy, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Pahang, Malaysia
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Koo SA, Jung Y, Um KA, Kim TH, Kim JY, Park CH. Clinical Feasibility of Deep Learning-Based Image Reconstruction on Coronary Computed Tomography Angiography. J Clin Med 2023; 12:jcm12103501. [PMID: 37240607 DOI: 10.3390/jcm12103501] [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: 03/19/2023] [Revised: 04/24/2023] [Accepted: 05/14/2023] [Indexed: 05/28/2023] Open
Abstract
This study evaluated the feasibility of deep-learning-based image reconstruction (DLIR) on coronary computed tomography angiography (CCTA). By using a 20 cm water phantom, the noise reduction ratio and noise power spectrum were evaluated according to the different reconstruction methods. Then 46 patients who underwent CCTA were retrospectively enrolled. CCTA was performed using the 16 cm coverage axial volume scan technique. All CT images were reconstructed using filtered back projection (FBP); three model-based iterative reconstructions (MBIR) of 40%, 60%, and 80%; and three DLIR algorithms: low (L), medium (M), and high (H). Quantitative and qualitative image qualities of CCTA were compared according to the reconstruction methods. In the phantom study, the noise reduction ratios of MBIR-40%, MBIR-60%, MBIR-80%, DLIR-L, DLIR-M, and DLIR-H were 26.7 ± 0.2%, 39.5 ± 0.5%, 51.7 ± 0.4%, 33.1 ± 0.8%, 43.2 ± 0.8%, and 53.5 ± 0.1%, respectively. The pattern of the noise power spectrum of the DLIR images was more similar to FBP images than MBIR images. In a CCTA study, CCTA yielded a significantly lower noise index with DLIR-H reconstruction than with the other reconstruction methods. DLIR-H showed a higher SNR and CNR than MBIR (p < 0.05). The qualitative image quality of CCTA with DLIR-H was significantly higher than that of MBIR-80% or FBP. The DLIR algorithm was feasible and yielded a better image quality than the FBP or MBIR algorithms on CCTA.
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Affiliation(s)
- Seul Ah Koo
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Yunsub Jung
- Research Team, GE Healthcare Korea, Seoul 04637, Republic of Korea
| | - Kyoung A Um
- Research Team, GE Healthcare Korea, Seoul 04637, Republic of Korea
| | - Tae Hoon Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Ji Young Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Chul Hwan Park
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
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Kataria B, Öman J, Sandborg M, Smedby Ö. Learning effects in visual grading assessment of model-based reconstruction algorithms in abdominal Computed Tomography. Eur J Radiol Open 2023; 10:100490. [PMID: 37207049 PMCID: PMC10189366 DOI: 10.1016/j.ejro.2023.100490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/06/2023] [Accepted: 05/01/2023] [Indexed: 05/21/2023] Open
Abstract
Objectives Images reconstructed with higher strengths of iterative reconstruction algorithms may impair radiologists' subjective perception and diagnostic performance due to changes in the amplitude of different spatial frequencies of noise. The aim of the present study was to ascertain if radiologists can learn to adapt to the unusual appearance of images produced by higher strengths of Advanced modeled iterative reconstruction algorithm (ADMIRE). Methods Two previously published studies evaluated the performance of ADMIRE in non-contrast and contrast-enhanced abdominal CT. Images from 25 (first material) and 50 (second material) patients, were reconstructed with ADMIRE strengths 3, 5 (AD3, AD5) and filtered back projection (FBP). Radiologists assessed the images using image criteria from the European guidelines for quality criteria in CT. To ascertain if there was a learning effect, new analyses of data from the two studies was performed by introducing a time variable in the mixed-effects ordinal logistic regression model. Results In both materials, a significant negative attitude to ADMIRE 5 at the beginning of the viewing was strengthened during the progress of the reviews for both liver parenchyma (first material: -0.70, p < 0.01, second material: -0.96, p < 0.001) and overall image quality (first material:-0.59, p < 0.05, second material::-1.26, p < 0.001). For ADMIRE 3, an early positive attitude for the algorithm was noted, with no significant change over time for all criteria except one (overall image quality), where a significant negative trend over time (-1.08, p < 0.001) was seen in the second material. Conclusions With progression of reviews in both materials, an increasing dislike for ADMIRE 5 images was apparent for two image criteria. In this time perspective (weeks or months), no learning effect towards accepting the algorithm could be demonstrated.
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Affiliation(s)
- Bharti Kataria
- Department of Radiology, Linköping University, Linköping, Sweden
- Department of Health, Medicine & Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science & Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Jenny Öman
- Department of Radiology, Linköping University, Linköping, Sweden
- Department of Health, Medicine & Caring Sciences, Linköping University, Linköping, Sweden
| | - Michael Sandborg
- Department of Health, Medicine & Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science & Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Medical Physics, Linköping University, Linköping, Sweden
| | - Örjan Smedby
- Department of Biomedical Engineering and Health Systems (MTH), KTH Royal Institute of Technology, Stockholm, Sweden
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Nagayama Y, Iwashita K, Maruyama N, Uetani H, Goto M, Sakabe D, Emoto T, Nakato K, Shigematsu S, Kato Y, Takada S, Kidoh M, Oda S, Nakaura T, Hatemura M, Ueda M, Mukasa A, Hirai T. Deep learning-based reconstruction can improve the image quality of low radiation dose head CT. Eur Radiol 2023; 33:3253-3265. [PMID: 36973431 DOI: 10.1007/s00330-023-09559-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 12/06/2022] [Accepted: 02/06/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES To evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images. METHODS This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured. RESULTS The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively. CONCLUSION DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time. KEY POINTS • For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. • The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s). • Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.
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Affiliation(s)
- Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan.
| | - Koya Iwashita
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Natsuki Maruyama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Makoto Goto
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Daisuke Sakabe
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Takafumi Emoto
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Kengo Nakato
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Shinsuke Shigematsu
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Yuki Kato
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Sentaro Takada
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masahiro Hatemura
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Mitsuharu Ueda
- Department of Neurology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Akitake Mukasa
- Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
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Xue G, Liu H, Cai X, Zhang Z, Zhang S, Liu L, Hu B, Wang G. Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors. Front Oncol 2023; 13:1167745. [PMID: 37091167 PMCID: PMC10113560 DOI: 10.3389/fonc.2023.1167745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/21/2023] [Indexed: 04/08/2023] Open
Abstract
ObjectiveTo evaluate the impact of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) on abdominal CT radiomic features acquired in portal venous phase in liver tumor patients.MethodsSixty patients with liver tumors who underwent contrast-enhanced abdominal CT were retrospectively enrolled. Six groups including filtered back projection (FBP), ASIR-V (30%, 70%) and DLIR at low (DLIR-L), medium (DLIR-M and high (DLIR-H), were reconstructed using portal venous phase data. CT-based radiomic features (first-order, texture and wavelet features) were extracted from 2D and 3D liver tumors, peritumor and liver parenchyma. All features were analyzed for comparison. P < 0.05 indicated statistically different. The consistency of 3D lesion feature extraction was assessed by calculating intraclass correlation coefficient (ICC).ResultsDifferent reconstruction algorithms influenced most radiomic features. The percentages of first-order, texture and wavelet features without statistical difference among 2D and 3D lesions, peritumor and liver parenchyma for all six groups were 27.78% (5/18), 5.33% (4/75) and 5.56% (1/18), respectively (all p > 0.05), and they decreased while the level of reconstruction strengthened for both ASIR-V and DLIR. Compared with FBP, the features of ASIR-V30% and 70% without statistical difference decreased from 71.31% to 23.95%, and DLIR-L, DLIR-M, and DLIR-H decreased from 31.65% to 27.11% and 23.73%. Among texture features, unaffected features of peritumor were larger than those of lesions and liver parenchyma, and unaffected 3D lesions features were larger than those of 2D lesions. The consistency of 3D lesion first-order features was excellent, with intra- and inter-observer ICCs ranging from 0.891 to 0.999 and 0.880 to 0.998.ConclusionsBoth ASIR-V and DLIR algorithms with different strengths influenced the radiomic features of abdominal CT images in portal venous phase, and the influences aggravated as reconstruction strength increased.
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Affiliation(s)
- Gongbo Xue
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- Graduate School, Dalian Medical University, Dalian, China
| | - Hongyan Liu
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Xiaoyi Cai
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- Graduate School, Dalian Medical University, Dalian, China
| | - Zhen Zhang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Shuai Zhang
- CT Imaging Research Center, GE Healthcare China, Shanghai, China
| | - Ling Liu
- CT Imaging Research Center, GE Healthcare China, Shanghai, China
| | - Bin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Guohua Wang, ; Bin Hu,
| | - Guohua Wang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
- *Correspondence: Guohua Wang, ; Bin Hu,
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Watanabe R, Zensho A, Ohishi Y, Funama Y. Image-quality characteristics in the longitudinal direction from different image-reconstruction algorithms during single-rotation volume acquisition on head computed tomography: A phantom study. Acta Radiol Open 2023; 12:20584601231168986. [PMID: 37089818 PMCID: PMC10116848 DOI: 10.1177/20584601231168986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 03/23/2023] [Indexed: 04/08/2023] Open
Abstract
Background A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. Purpose To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms. Material and methods Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction ( Z-axis). Results The MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center. Conclusion Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis.
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Affiliation(s)
- Ryo Watanabe
- Department of Radiology, Hospital of the University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Ayako Zensho
- Department of Radiology, Hospital of the University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yoshitaka Ohishi
- Department of Radiology, Hospital of the University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
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Zhuang T, Gibbard G, Duan X, Tan J, Park Y, Lin MH, Sun Z, Oderinde OM, Lu W, Reynolds R, Godley A, Pompos A, Dan T, Garant A, Iyengar P, Timmerman R, Jiang S, Cai B. Evaluation of fan-beam kilovoltage computed tomography image quality on a novel biological-guided radiotherapy platform. Phys Imaging Radiat Oncol 2023; 26:100438. [PMID: 37342208 PMCID: PMC10277913 DOI: 10.1016/j.phro.2023.100438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 03/29/2023] [Accepted: 04/05/2023] [Indexed: 06/22/2023] Open
Abstract
Background and Purpose A recently developed biology-guided radiotherapy platform, equipped with positron emission tomography (PET) and computed tomography (CT), provides both anatomical and functional image guidance for radiotherapy. This study aimed to characterize performance of the kilovoltage CT (kVCT) system on this platform using standard quality metrics measured on phantom and patient images, using CT simulator images as reference. Materials and Methods Image quality metrics, including spatial resolution/modular transfer function (MTF), slice sensitivity profile (SSP), noise performance and image uniformity, contrast-noise ratio (CNR) and low-contrast resolution, geometric accuracy, and CT number (HU) accuracy, were evaluated on phantom images. Patient images were evaluated mainly qualitatively. Results On phantom images the MTF10% is about 0.68 lp/mm for kVCT in PET/CT Linac. The SSP agreed with nominal slice thickness within 0.7 mm. The diameter of the smallest visible target (1% contrast) is about 5 mm using medium dose mode. The image uniformity is within 2.0 HU. The geometric accuracy tests passed within 0.5 mm. Relative to CT simulator images, the noise is generally higher and the CNR is lower in PET/CT Linac kVCT images. The CT number accuracy is comparable between the two systems with maximum deviation from the phantom manufacturer range within 25 HU. On patient images, higher spatial resolution and image noise are observed on PET/CT Linac kVCT images. Conclusions Major image quality metrics of the PET/CT Linac kVCT were within vendor-recommended tolerances. Better spatial resolution but higher noise and better/comparable low contrast visibility were observed as compared to a CT simulator when images were acquired with clinical protocols.
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Affiliation(s)
- Tingliang Zhuang
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Grant Gibbard
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Xinhui Duan
- Department of Radiology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Jun Tan
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Yang Park
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Zhihui Sun
- RefleXion Medical, Inc, Hayward, CA, USA
| | | | - Weiguo Lu
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Robert Reynolds
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Andrew Godley
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Arnold Pompos
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Tu Dan
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Aurelie Garant
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Puneeth Iyengar
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Robert Timmerman
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
| | - Bin Cai
- Department of Radiation Oncology, University of Texas- Southwestern Medical Center, Dallas, USA
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Yang L, Li Z, Ge R, Zhao J, Si H, Zhang D. Low-Dose CT Denoising via Sinogram Inner-Structure Transformer. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:910-921. [PMID: 36331637 DOI: 10.1109/tmi.2022.3219856] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Low-Dose Computed Tomography (LDCT) technique, which reduces the radiation harm to human bodies, is now attracting increasing interest in the medical imaging field. As the image quality is degraded by low dose radiation, LDCT exams require specialized reconstruction methods or denoising algorithms. However, most of the recent effective methods overlook the inner-structure of the original projection data (sinogram) which limits their denoising ability. The inner-structure of the sinogram represents special characteristics of the data in the sinogram domain. By maintaining this structure while denoising, the noise can be obviously restrained. Therefore, we propose an LDCT denoising network namely Sinogram Inner-Structure Transformer (SIST) to reduce the noise by utilizing the inner-structure in the sinogram domain. Specifically, we study the CT imaging mechanism and statistical characteristics of sinogram to design the sinogram inner-structure loss including the global and local inner-structure for restoring high-quality CT images. Besides, we propose a sinogram transformer module to better extract sinogram features. The transformer architecture using a self-attention mechanism can exploit interrelations between projections of different view angles, which achieves an outstanding performance in sinogram denoising. Furthermore, in order to improve the performance in the image domain, we propose the image reconstruction module to complementarily denoise both in the sinogram and image domain.
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Svalkvist A, Fagman E, Vikgren J, Ku S, Diniz MO, Norrlund RR, Johnsson ÅA. Evaluation of deep-learning image reconstruction for chest CT examinations at two different dose levels. J Appl Clin Med Phys 2023; 24:e13871. [PMID: 36583696 PMCID: PMC10018655 DOI: 10.1002/acm2.13871] [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: 04/07/2022] [Revised: 11/17/2022] [Accepted: 11/29/2022] [Indexed: 12/31/2022] Open
Abstract
AIMS The aims of the present study were to, for both a full-dose protocol and an ultra-low dose (ULD) protocol, compare the image quality of chest CT examinations reconstructed using TrueFidelity (Standard kernel) with corresponding examinations reconstructed using ASIR-V (Lung kernel) and to evaluate if post-processing using an edge-enhancement filter affects the noise level, spatial resolution and subjective image quality of clinical images reconstructed using TrueFidelity. METHODS A total of 25 patients were examined with both a full-dose protocol and an ULD protocol using a GE Revolution APEX CT system (GE Healthcare, Milwaukee, USA). Three different reconstructions were included in the study: ASIR-V 40%, DLIR-H, and DLIR-H with additional post-processing using an edge-enhancement filter (DLIR-H + E2). Five observers assessed image quality in two separate visual grading characteristics (VGC) studies. The results from the studies were statistically analyzed using VGC Analyzer. Quantitative evaluations were based on determination of two-dimensional power spectrum (PS), contrast-to-noise ratio (CNR), and spatial resolution in the reconstructed patient images. RESULTS For both protocols, examinations reconstructed using TrueFidelity were statistically rated equal to or significantly higher than examinations reconstructed using ASIR-V 40%, but the ULD protocol benefitted more from TrueFidelity. In general, no differences in observer ratings were found between DLIR-H and DLIR-H + E2. For the three investigated image reconstruction methods, ASIR-V 40% showed highest noise and spatial resolution and DLIR-H the lowest, while the CNR was highest in DLIR-H and lowest in ASIR-V 40%. CONCLUSION The use of TrueFidelity for image reconstruction resulted in higher ratings on subjective image quality than ASIR-V 40%. The benefit of using TrueFidelity was larger for the ULD protocol than for the full-dose protocol. Post-processing of the TrueFidelity images using an edge-enhancement filter resulted in higher image noise and spatial resolution but did not affect the subjective image quality.
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Affiliation(s)
- Angelica Svalkvist
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Medical Radiation Sciences, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Erika Fagman
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jenny Vikgren
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sara Ku
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Micael Oliveira Diniz
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Rauni Rossi Norrlund
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Åse A Johnsson
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Lyu Q, Neph R, Sheng K. Tomographic detection of photon pairs produced from high-energy X-rays for the monitoring of radiotherapy dosing. Nat Biomed Eng 2023; 7:323-334. [PMID: 36280738 PMCID: PMC10038801 DOI: 10.1038/s41551-022-00953-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 09/14/2022] [Indexed: 01/07/2023]
Abstract
Measuring the radiation dose reaching a patient's body is difficult. Here we report a technique for the tomographic reconstruction of the location of photon pairs originating from the annihilation of positron-electron pairs produced by high-energy X-rays travelling through tissue. We used Monte Carlo simulations on pre-recorded data from tissue-mimicking phantoms and from a patient with a brain tumour to show the feasibility of this imaging modality, which we named 'pair-production tomography', for the monitoring of radiotherapy dosing. We simulated three image-reconstruction methods, one applicable to a pencil X-ray beam scanning through a region of interest, and two applicable to the excitation of tissue volumes via broad beams (with temporal resolution sufficient to identify coincident photon pairs via filtered back projection, or with higher temporal resolution sufficient for the estimation of a photon's time-of-flight). In addition to the monitoring of radiotherapy dosing, we show that image contrast resulting from pair-production tomography is highly proportional to the material's atomic number. The technique may thus also allow for element mapping and for soft-tissue differentiation.
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Affiliation(s)
- Qihui Lyu
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Ryan Neph
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Ke Sheng
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA.
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Agostini A, Borgheresi A, Mariotti F, Ottaviani L, Carotti M, Valenti M, Giovagnoni A. New frontiers in oncological imaging with Computed Tomography: from morphology to function. Semin Ultrasound CT MR 2023; 44:214-227. [PMID: 37245886 DOI: 10.1053/j.sult.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Guido G, Polici M, Nacci I, Bozzi F, De Santis D, Ubaldi N, Polidori T, Zerunian M, Bracci B, Laghi A, Caruso D. Iterative Reconstruction: State-of-the-Art and Future Perspectives. J Comput Assist Tomogr 2023; 47:244-254. [PMID: 36728734 DOI: 10.1097/rct.0000000000001401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
ABSTRACT Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.
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Affiliation(s)
- Gisella Guido
- From the Department of Surgical Medical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit, Sant'Andrea University Hospital, Rome, Italy
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Njølstad T, Schulz A, Jensen K, Andersen HK, Martinsen ACT. Improved image quality with deep learning reconstruction - a study on a semi-anthropomorphic upper-abdomen phantom. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2023; 5:100022. [PMID: 39076164 PMCID: PMC11265485 DOI: 10.1016/j.redii.2023.100022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 12/27/2022] [Indexed: 07/31/2024]
Abstract
Purpose To assess image quality of a deep learning reconstruction (DLR) algorithm across dose levels using a semi-anthropomorphic upper-abdominal phantom, and compare with filtered back projection (FBP) and hybrid iterative reconstruction (IR). Material and methods CT scans obtained at five dose levels (CTDIvol 5, 10, 15, 20 and 25 mGy) were reconstructed with FBP, hybrid IR (IR50, IR70 and IR90) and DLR of low (DLL), medium (DLM) and high strength (DLH) in 0.625 mm and 2.5 mm slices. CT number, homogeneity, noise, contrast, contrast-to-noise ratio (CNR), noise texture deviation (NTD; a measure of IR-specific artifacts), noise power spectrum (NPS) and task-based transfer function (TTF) were compared between reconstruction algorithms. Results CT numbers were highly consistent across reconstruction algorithms. Image noise was significantly reduced with higher levels of DLR. Noise texture (NPS and NTD) was with DLR maintained at comparable levels to FBP, contrary to increasing levels of hybrid IR. Images reconstructed with DLR of low and high strength in 0.625 mm slices showed similar noise characteristics to 2.5 mm slice FBP and IR50, respectively. Dose-reduction potential based on image noise with IR50 as reference was estimated to 35% for DLM and 74% for DLH. Conclusions The novel DLR algorithm demonstrates robust noise reduction with maintained noise texture characteristics despite higher algorithm strength, and may have overcome important limitations of IR. There may be potential for dose reduction and additional benefit from thin-slice reconstruction.
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Affiliation(s)
- Tormund Njølstad
- Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, Oslo 0450, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Anselm Schulz
- Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, Oslo 0450, Norway
| | - Kristin Jensen
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Hilde K. Andersen
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Anne Catrine T. Martinsen
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Sunnaas Rehabilitation Hospital, Nesodden, Norway
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Lyu P, Liu N, Harrawood B, Solomon J, Wang H, Chen Y, Rigiroli F, Ding Y, Schwartz FR, Jiang H, Lowry C, Wang L, Samei E, Gao J, Marin D. Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely? Eur Radiol 2023; 33:1629-1640. [PMID: 36323984 DOI: 10.1007/s00330-022-09206-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/28/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). METHODS A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. RESULTS The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001). CONCLUSION DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. KEY POINTS • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).
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Affiliation(s)
- Peijie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China.,Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
| | - Nana Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Brian Harrawood
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Huixia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China
| | - Francesca Rigiroli
- Beth Israel Deaconess Medical Center Department of Radiology, Harvard Medical School, 1 Deaconess Rd, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Yuqin Ding
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA.,Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 20032, China
| | - Fides Regina Schwartz
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
| | - Hanyu Jiang
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA.,Department of Radiology, West China Hospital of Sichuan University, 37 Guoxue Alley, Chengdu, 610041, Sichuan Province, China
| | - Carolyn Lowry
- Clinical Imaging Physics Group, Duke University Health System, 2424 Erwin Rd, Ste. 302, Durham, NC, 27705, USA
| | - Luotong Wang
- CT Imaging Research Center, GE Healthcare China, No.1 Tongji South Road, Beijing, 100176, China
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China.
| | - Daniele Marin
- Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA
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Shibata H, Matsubara K, Asada Y, Takemura A, Kozawa I. Physical and visual evaluations of CT image quality of large low-contrast objects with visual model-based iterative reconstruction technique: a phantom study. Phys Eng Sci Med 2023; 46:141-150. [PMID: 36508073 DOI: 10.1007/s13246-022-01205-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 11/30/2022] [Indexed: 12/14/2022]
Abstract
We aimed to verify whether the image quality of large low-contrast objects can be improved using visual model-based iterative reconstruction (VMR) while maintaining the visibility of conventional filtered back projection (FBP) and reducing radiation dose through physical and visual evaluation. A 64-row multi-slice CT system with SCENARIA View (FUJIFILM healthcare Corp. Tokyo, Japan) was used. The noise power spectrum (NPS), task-based transfer function (TTF), and signal-to-noise ratio (SNR) were physically evaluated. A low contrast object as a substitute for a liver mass was visually evaluated. In the noise measurement, STD1 showed an 18% lower noise compared to FBP. STR4 was able to reduce noise by 58% compared to FBP. The NPS of VMR was similar to those of FBP from low to high spatial frequency. The NPS of VMR reconstructions showed a similar variation with frequency as FBP reconstructions. STD1 showed the highest 10% TTF, and higher 10% TTF was observed with lower VMR level. The SNR of VMR was close to that of FBP, and higher SNR was observed with higher VMR level. In the results of the visual evaluation, there was no significant difference in visual evaluation between STD1 and FBP (p = 0.99) and between STD2 and FBP (p = 0.56). We found that the NPS of VMR images was similar to that of FBP images, and it can reduce noise and radiation dose by 25% and 50%, respectively, without decreasing the visual image quality compared to FBP.
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Affiliation(s)
- Hideki Shibata
- Department of Radiological Technology, Toyota Kosei Hospital, 500-1 Ibobara Josui, Toyota, Aichi, 470-0396, Japan.
- Department of Quantum Medical Technology, Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Kosuke Matsubara
- Department of Quantum Medical Technology, Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan
| | - Yasuki Asada
- School of Health Sciences, Fujita Health University, Toyoake, Aichi, 470-1192, Japan
| | - Akihiro Takemura
- Department of Quantum Medical Technology, Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan
| | - Isao Kozawa
- Department of Radiological Technology, Toyota Kosei Hospital, 500-1 Ibobara Josui, Toyota, Aichi, 470-0396, Japan
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Katsuyama Y, Kojima T, Shirasaka T, Kondo M, Kato T. Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study. Radiol Phys Technol 2023; 16:77-84. [PMID: 36583827 DOI: 10.1007/s12194-022-00695-x] [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: 09/15/2022] [Revised: 12/19/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022]
Abstract
PURPOSE We assessed the physical properties of virtual monochromatic images (VMIs) obtained with different energy levels in various contrast settings and radiation doses using deep learning-based spectral computed tomography (DL-Spectral CT) and compared the results with those from single-energy CT (SECT) imaging. MATERIALS AND METHODS A Catphan® 600 phantom was scanned by DL-Spectral CT at various radiation doses. We reconstructed the VMIs obtained at 50, 70, and 100 keV. SECT (120 kVp) images were acquired at the same radiation doses. The standard deviations of the CT number and noise power spectrum (NPS) were calculated for noise characterization. We evaluated the spatial resolution by determining the 10% task-based transfer function (TTF) level, and we assessed the task-based detectability index (d'). RESULTS Regardless of the radiation dose, the noise was the lowest at 70 keV VMI. The NPS showed that the noise amplitude at all spatial frequencies was the lowest among other VMI and 120 kVp images. The spatial resolution was higher for 70 keV VMI compared to the other VMIs, except for high-contrast objects. The d' of 70 keV VMI was the highest among the VMI and 120 kVp images at all radiation doses and contrast settings. The d' of the 70 keV VMIs at the minimum dose was higher than that at the maximum dose in any other image. CONCLUSION The physical properties of the DL-Spectral CT VMIs varied with the energy level. The 70 keV VMI had the highest detectability by far among the VMI and 120-kVp images. DL-Spectral CT may be useful to reduce radiation doses.
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Affiliation(s)
- Yuna Katsuyama
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.
| | - Tsukasa Kojima
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.,Department of Health Sciences, Graduate school of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan
| | - Takashi Shirasaka
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan
| | - Masatoshi Kondo
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan
| | - Toyoyuki Kato
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan
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Goto M, Nagayama Y, Sakabe D, Emoto T, Kidoh M, Oda S, Nakaura T, Taguchi N, Funama Y, Takada S, Uchimura R, Hayashi H, Hatemura M, Kawanaka K, Hirai T. Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT. Acad Radiol 2023; 30:431-440. [PMID: 35738988 DOI: 10.1016/j.acra.2022.04.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/18/2022] [Accepted: 04/30/2022] [Indexed: 01/25/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR). MATERIALS AND METHODS An anthropomorphic chest phantom was scanned on a 320-row scanner at 50-mA (low-dose-CT 1 [LDCT-1]), 25-mA (LDCT-2), and 10-mA (ULDCT). LDCT were reconstructed with HIR; ULDCT images were reconstructed with HIR (ULDCT-HIR), MBIR (ULDCT-MBIR), and DLR (ULDCT-DLR). Image noise and contrast-to-noise ratio (CNR) were quantified. With the LDCT images as reference standards, ULDCT image qualities were subjectively scored on a 5-point scale (1 = substantially inferior to LDCT-2, 3 = comparable to LDCT-2, 5 = comparable to LDCT-1). For task-based image quality analyses, a physical evaluation phantom was scanned at seven doses to achieve the noise levels equivalent to chest phantom; noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated. Clinical ULDCT (10-mA) images obtained in 14 nonobese patients were reconstructed with HIR, MBIR, and DLR; the subjective acceptability was ranked. RESULTS Image noise was lower and CNR was higher in ULDCT-DLR and ULDCT-MBIR than in LDCT-1, LDCT-2, and ULDCT-HIR (p < 0.01). The overall quality of ULDCT-DLR was higher than of ULDCT-HIR and ULDCT-MBIR (p < 0.01), and almost comparable with that of LDCT-2 (mean score: 3.4 ± 0.5). DLR yielded the highest NPS peak frequency and TTF50% for high-contrast object. In clinical ULDCT images, the subjective acceptability of DLR was higher than of HIR and MBIR (p < 0.01). CONCLUSION DLR optimized for lung CT improves image quality and provides possible greater dose optimization opportunity than HIR and MBIR.
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Affiliation(s)
- Makoto Goto
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Daisuke Sakabe
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto 860-8556, Japan
| | - Takafumi Emoto
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Narumi Taguchi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, Chuo-ku, Kumamoto 862-0976, Japan
| | - Sentaro Takada
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Ryutaro Uchimura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Hidetaka Hayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Masahiro Hatemura
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto 860-8556, Japan
| | - Koichi Kawanaka
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan
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Chen J, Chen S, Wee L, Dekker A, Bermejo I. Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review. Phys Med Biol 2023; 68. [PMID: 36753766 DOI: 10.1088/1361-6560/acba74] [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: 08/05/2022] [Accepted: 02/08/2023] [Indexed: 02/10/2023]
Abstract
Purpose. There is a growing number of publications on the application of unpaired image-to-image (I2I) translation in medical imaging. However, a systematic review covering the current state of this topic for medical physicists is lacking. The aim of this article is to provide a comprehensive review of current challenges and opportunities for medical physicists and engineers to apply I2I translation in practice.Methods and materials. The PubMed electronic database was searched using terms referring to unpaired (unsupervised), I2I translation, and medical imaging. This review has been reported in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. From each full-text article, we extracted information extracted regarding technical and clinical applications of methods, Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) study type, performance of algorithm and accessibility of source code and pre-trained models.Results. Among 461 unique records, 55 full-text articles were included in the review. The major technical applications described in the selected literature are segmentation (26 studies), unpaired domain adaptation (18 studies), and denoising (8 studies). In terms of clinical applications, unpaired I2I translation has been used for automatic contouring of regions of interest in MRI, CT, x-ray and ultrasound images, fast MRI or low dose CT imaging, CT or MRI only based radiotherapy planning, etc Only 5 studies validated their models using an independent test set and none were externally validated by independent researchers. Finally, 12 articles published their source code and only one study published their pre-trained models.Conclusion. I2I translation of medical images offers a range of valuable applications for medical physicists. However, the scarcity of external validation studies of I2I models and the shortage of publicly available pre-trained models limits the immediate applicability of the proposed methods in practice.
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Affiliation(s)
- Junhua Chen
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands
| | - Shenlun Chen
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands
| | - Inigo Bermejo
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands
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Chen BJ, Zeng ZX, Zhao YX, Wu MW, Bao X, Li T, Feng J, Li ZJ, Zhang GL, Feng R. Angioplasty for Supra-Aortic Arterial Lesions from Takayasu Arteritis: Efficacy of Cutting Balloon Angioplasty Versus Conventional Balloon Angioplasty. Ann Vasc Surg 2023:S0890-5096(23)00100-0. [PMID: 36805427 DOI: 10.1016/j.avsg.2023.01.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 01/17/2023] [Accepted: 01/27/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND This retrospective study aimed to evaluate the safety and efficacy of cutting balloon angioplasty and conventional balloon angioplasty in supra-aortic arterial lesions caused by Takayasu arteritis. METHODS A total of 46 patients with supra-aortic arterial lesions between January 2011 and December 2018 were included. Cutting balloon angioplasty was applied for 17 patients with 24 supra-aortic arterial lesions (group A), while 29 patients with 36 supra-aortic arterial lesions received conventional balloon angioplasty (group B). The preoperative clinical manifestation, operation result, and postoperative outcomes were recorded and compared in the 2 groups. RESULTS Dizziness, visual disturbance, and unequal/absent pulses were the most common manifestations. The technical success of revascularization was 93.5% (43/46) in patients and 93.3% (56/60) in lesions. The stent implantation rate in group A was significantly lower than that in group B (4.2% vs. 50% in lesions, P < 0.05). Restenosis was the most common complication in both groups. Although the early (≤30 days) and late (>30 days) complications in group A were less than those in group B, there was no significant difference between the 2 groups (P > 0.05). Moreover, the primary-assisted patency of cutting balloon angioplasty and conventional balloon angioplasty at 1, 2, and 5 years were 66.7%, 62.5%, and 62.5% and 61.1%, 58.2%, and 49.8%, there was no significant difference between the 2 groups (P > 0.05), respectively. CONCLUSIONS Compared with conventional balloon angioplasty, cutting balloon angioplasty could be considered a safe and effective alternative for supra-aortic arterial lesions caused by Takayasu arteritis, demonstrating better patency and clinical benefit.
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Affiliation(s)
- Bing-Ji Chen
- Department of Emergency Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Zhao-Xiang Zeng
- Department of Vascular Surgery, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yu-Xi Zhao
- Department of Vascular Surgery, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Ming-Wei Wu
- Department of Vascular Surgery, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xianhao Bao
- Department of Vascular Surgery, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Tao Li
- Department of Vascular Surgery, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jiaxuan Feng
- Department of Vascular Surgery, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Zhen-Jiang Li
- Department of Vascular Surgery, The First Affiliated Hospital of the Medical School of Zhejiang University, Hangzhou, Zhejiang, China.
| | - Guang-Lin Zhang
- Department of General Surgery, Jiuquan City People's Hospital, Jiuquan, Gansu, China.
| | - Rui Feng
- Department of Vascular Surgery, Shanghai General Hospital, Shanghai, China.
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Yang L, Liu H, Han J, Xu S, Zhang G, Wang Q, Du Y, Yang F, Zhao X, Shi G. Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software. Clin Radiol 2023:S0009-9260(23)00031-4. [PMID: 36948944 DOI: 10.1016/j.crad.2023.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 01/04/2023] [Accepted: 01/15/2023] [Indexed: 02/05/2023]
Abstract
AIM To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD). MATERIALS AND METHODS A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference. RESULTS Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively. CONCLUSION Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.
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Affiliation(s)
- L Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - H Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - J Han
- United Imaging Healthcare, Shanghai, China
| | - S Xu
- United Imaging Healthcare, Shanghai, China
| | - G Zhang
- United Imaging Healthcare, Shanghai, China
| | - Q Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Y Du
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - F Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - X Zhao
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - G Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
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Zhong J, Xia Y, Chen Y, Li J, Lu W, Shi X, Feng J, Yan F, Yao W, Zhang H. Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study. Eur Radiol 2023; 33:812-824. [PMID: 36197579 DOI: 10.1007/s00330-022-09119-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/26/2022] [Accepted: 08/17/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness. METHODS A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified. RESULTS DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively. CONCLUSIONS DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified. KEY POINTS • DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Jianxing Feng
- Haohua Technology Co., Ltd., Shanghai, 201100, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Noda Y, Takai Y, Asano M, Yamada N, Seko T, Kawai N, Kaga T, Miyoshi T, Hyodo F, Kato H, Matsuo M. Comparison of image quality and pancreatic ductal adenocarcinoma conspicuity between the low-kVp and dual-energy CT reconstructed with deep-learning image reconstruction algorithm. Eur J Radiol 2023; 159:110685. [PMID: 36603479 DOI: 10.1016/j.ejrad.2022.110685] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/28/2022] [Indexed: 01/01/2023]
Abstract
PURPOSE To compare the image quality and conspicuity of pancreatic ductal adenocarcinoma (PDAC) between the low-kVp and dual-energy pancreatic protocol CT reconstructed with deep-learning image reconstruction (DLIR). METHOD A cohort of 111 consecutive patients (median age, 72 years; 56 men) undergoing a pancreatic protocol CT were retrospectively analyzed. Among them, 58 patients underwent 80-kVp CT (80-kVp group), and 53 patients underwent dual-energy CT and reconstructed at 40-keV (40-keV group). The medium-strength level of DLIR were used in both groups. Quantitative measurements, qualitative image quality, PDAC conspicuity, and dose-length product (DLP) were compared between the two groups using Mann-Whitney U test. RESULTS A total of 20 and 16 PDACs were found in the 80-kVp and 40-keV groups, respectively. CT numbers of the vasculatures and parenchymal organs (P <.001 for all) and the background noise at both pancreatic and portal venous phases (P <.001) were higher in the 40-keV group than in the 80-kVp group. The signal-to-noise ratio (SNR) of all anatomical structures (P <.001-0.005), except for the liver in reviewer 2 (P =.47), and the tumor-to-pancreas contrast-to-noise ratio (CNR; P <.001-0.01) were higher in the 40-keV group than in the 80-kVp group. No difference was found in the image quality at both phases (P =.30-0.90). PDAC conspicuity was better in the 40-keV group than in the 80-kVp group (P =.007-0.03). DLP at pancreatic (275 vs. 313 mGy*cm; P =.05) and portal venous phases (743 vs. 766 mGy*cm; P =.20) was comparable between the two groups. CONCLUSION Under the same DLP, virtual monoenergetic images at 40-keV demonstrated higher SNR and tumor-to-pancreas CNR and better PDAC conspicuity compared to the 80-kVp setting.
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Affiliation(s)
- Yoshifumi Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
| | - Yukiko Takai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Masashi Asano
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Nao Yamada
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Takuya Seko
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Nobuyuki Kawai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Tetsuro Kaga
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Toshiharu Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Fuminori Hyodo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan; Institute for Advanced Study, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Hiroki Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
| | - Masayuki Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
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79
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"Image quality evaluation of the Precise image CT deep learning reconstruction algorithm compared to Filtered Back-projection and iDose 4: a phantom study at different dose levels". Phys Med 2023; 106:102517. [PMID: 36669326 DOI: 10.1016/j.ejmp.2022.102517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/08/2022] [Accepted: 12/27/2022] [Indexed: 01/19/2023] Open
Abstract
PURPOSE To characterize the performance of the Precise Image (PI) deep learning reconstruction (DLR) algorithm for abdominal Computed Tomography (CT) imaging. METHODS CT images of the Catphan-600 phantom (equipped with an external annulus) were acquired using an abdominal protocol at four dose levels and reconstructed using FBP, iDose4 (levels 2,5) and PI ('Soft Tissue' definition, levels 'Sharper','Sharp','Standard','Smooth','Smoother'). Image noise, image non-uniformity, noise power spectrum (NPS), target transfer function (TTF), detectability index (d'), CT numbers accuracy and image histograms were analyzed. RESULTS The behavior of the PI algorithm depended strongly on the selected level of reconstruction. The phantom analysis suggested that the PI image noise decreased linearly by varying the level of reconstruction from Sharper to Smoother, expressing a noise reduction up to 80% with respect to FBP. Additionally, the non-uniformity decreased, the histograms became narrower, and d' values increased as PI reconstruction levels changed from Sharper to Smoother. PI had no significant impact on the average CT number of different contrast objects. The conventional FBP NPS was deeply altered only by Smooth and Smoother levels of reconstruction. Furthermore, spatial resolution was found to be dose- and contrast-dependent, but in each analyzed condition it was greater than or comparable to FBP and iDose4 TTFs. CONCLUSIONS The PI algorithm can reduce image noise with respect to FBP and iDose4; spatial resolution, CT numbers and image uniformity are generally preserved by the algorithm but changes in NPS for the Smooth and Smoother levels need to be considered in protocols implementation.
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80
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Guo Q, Liu H, Li X, Wu M, Li J, Zhang X. A rare middle aortic syndrome with celiac trunk, superior mesenteric and bilateral renal artery involvement. Heliyon 2023; 9:e13022. [PMID: 36798781 PMCID: PMC9925870 DOI: 10.1016/j.heliyon.2023.e13022] [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: 09/28/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/20/2023] Open
Abstract
Middle aortic syndrome (MAS) is a rare atypical aortic coarctation (AC), often accompanied by refractory renal hypertension, which eventually leads to death from congestive heart failure, stroke or hypertensive encephalopathy. Computed tomography angiography (CTA) has unique advantages in assessing aortic stenosis and splanchnic artery abnormalities. Prompt aortic bypass surgery can relieve symptoms and improve quality of life. In this study, we report a patient with MAS diagnosed by CTA and follow-up after thoracoabdominal aortic bypass grafting.
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81
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Pouget E, Dedieu V. Comparison of supervised-learning approaches for designing a channelized observer for image quality assessment in CT. Med Phys 2023. [PMID: 36647620 DOI: 10.1002/mp.16227] [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: 04/29/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The current paradigm for evaluating computed tomography (CT) system performance relies on a task-based approach. As the Hotelling observer (HO) provides an upper bound of observer performances in specific signal detection tasks, the literature advocates HO use for optimization purposes. However, computing the HO requires calculating the inverse of the image covariance matrix, which is often intractable in medical applications. As an alternative, dimensionality reduction has been extensively investigated to extract the task-relevant features from the raw images. This can be achieved by using channels, which yields the channelized-HO (CHO). The channels are only considered efficient when the channelized observer (CO) can approximate its unconstrained counterpart. Previous work has demonstrated that supervised learning-based methods can usually benefit CO design, either for generating efficient channels using partial least squares (PLS) or for replacing the Hotelling detector with machine-learning (ML) methods. PURPOSE Here we investigated the efficiency of a supervised ML-algorithm used to design a CO for predicting the performance of unconstrained HO. The ML-algorithm was applied either (1) in the estimator for dimensionality reduction, or (2) in the detector function. METHODS A channelized support vector machine (CSVM) was employed and compared against the CHO in terms of ability to predict HO performances. Both the CSVM and the CHO were estimated with channels derived from the singular value decomposition (SVD) of the system operator, principal component analysis (PCA), and PLS. The huge variety of regularization strategies proposed by CT system vendors for statistical image reconstruction (SIR) make the generalization capability of an observer a key point to consider upfront of implementation in clinical practice. To evaluate the generalization properties of the observers, we adopted a 2-step testing process: (1) achieved with the same regularization strategy (as in the training phase) and (2) performed using different reconstruction properties. We generated simulated- signal-known-exactly/background-known-exactly (SKE/BKE) tasks in which different noise structures were generated using Markov random field (MRF) regularizations using either a Green or a quadratic, function. RESULTS The CSVM outperformed the CHO for all types of channels and regularization strategies. Furthermore, even though both COs generalized well to images reconstructed with the same regularization strategy as the images considered in the training phase, the CHO failed to generalize to images reconstructed differently whereas the CSVM managed to successfully generalize. Lastly, the proposed CSVM observer used with PCA channels outperformed the CHO with PLS channels while using a smaller training data set. CONCLUSION These results argue for introducing the supervised-learning paradigm in the detector function rather than in the operator of the channels when designing a CO to provide an accurate estimate of HO performance. The CSVM with PCA channels proposed here could be used as a surrogate for HO in image quality assessment.
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Affiliation(s)
- Eléonore Pouget
- Department of Medical Physics, Jean Perrin Comprehensive Cancer Center, Clermont-Ferrand, France.,Clermont-Ferrand University, UMR 1240 INSERM IMoST, Clermont-Ferrand, France
| | - Véronique Dedieu
- Department of Medical Physics, Jean Perrin Comprehensive Cancer Center, Clermont-Ferrand, France.,Clermont-Ferrand University, UMR 1240 INSERM IMoST, Clermont-Ferrand, France
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82
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Hu Y, Zheng Z, Yu H, Wang J, Yang X, Shi H. Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study. EJNMMI Phys 2023; 10:1. [PMID: 36592256 PMCID: PMC9807709 DOI: 10.1186/s40658-022-00521-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/20/2022] [Indexed: 01/03/2023] Open
Abstract
PURPOSE To investigate the feasibility of ultra-low-dose CT (ULDCT) reconstructed with the artificial intelligence iterative reconstruction (AIIR) algorithm in total-body PET/CT imaging. METHODS The study included both the phantom and clinical parts. An anthropomorphic phantom underwent CT imaging with ULDCT (10mAs) and standard-dose CT (SDCT) (120mAs), respectively. ULDCT was reconstructed with AIIR and hybrid iterative reconstruction (HIR) (expressed as ULDCT-AIIRphantom and ULDCT-HIRphantom), respectively, and SDCT was reconstructed with HIR (SDCT-HIRphantom) as control. In the clinical part, 52 patients with malignant tumors underwent the total-body PET/CT scan. ULDCT with AIIR (ULDCT-AIIR) and HIR (ULDCT-HIR), respectively, was reconstructed for PET attenuation correction, followed by the SDCT reconstructed with HIR (SDCT-HIR) for anatomical location. PET/CT images' quality was qualitatively assessed by two readers. The CTmean, as well as the CT standard deviation (CTsd), SUVmax, SUVmean, and the SUV standard deviation (SUVsd), was recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared. RESULTS The image quality of ULDCT-HIRphantom was inferior to the SDCT-HIRphantom, but no significant difference was found between the ULDCT-AIIRphantom and SDCT-HIRphantom. The subjective score of ULDCT-AIIR in the neck, chest and lower limb was equivalent to that of SDCT-HIR. Besides the brain and lower limb, the change rates of CTmean in thyroid, neck muscle, lung, mediastinum, back muscle, liver, lumbar muscle, first lumbar spine and sigmoid colon were -2.15, -1.52, 0.66, 2.97, 0.23, 8.91, 0.06, -4.29 and 8.78%, respectively, while all CTsd of ULDCT-AIIR was lower than that of SDCT-HIR. Except for the brain, the CNR of ULDCT-AIIR was the same as the SDCT-HIR, but the SNR was higher. The change rates of SUVmax, SUVmean and SUVsd were within [Formula: see text] 3% in all ROIs. For the lesions, the SUVmax, SUVsd and TBR showed no significant difference between PET-AIIR and PET-HIR. CONCLUSION The SDCT-HIR could not be replaced by the ULDCT-AIIR at date, but the AIIR algorithm decreased the image noise and increased the SNR, which can be implemented under special circumstances in PET/CT examination.
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Affiliation(s)
- Yan Hu
- grid.8547.e0000 0001 0125 2443Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Nuclear Medicine Institute of Fudan University, Shanghai, 200032 China ,grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, Shanghai, 200032 China
| | - Zhe Zheng
- grid.8547.e0000 0001 0125 2443Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Nuclear Medicine Institute of Fudan University, Shanghai, 200032 China ,grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, Shanghai, 200032 China
| | - Haojun Yu
- grid.8547.e0000 0001 0125 2443Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Nuclear Medicine Institute of Fudan University, Shanghai, 200032 China ,grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, Shanghai, 200032 China
| | - Jingyi Wang
- grid.497849.fUnited Imaging Healthcare Co., Ltd., Shanghai, China
| | - Xinlan Yang
- grid.497849.fUnited Imaging Healthcare Co., Ltd., Shanghai, China
| | - Hongcheng Shi
- grid.8547.e0000 0001 0125 2443Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Nuclear Medicine Institute of Fudan University, Shanghai, 200032 China ,grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, Shanghai, 200032 China
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83
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Oppenheimer J, Bressem KK, Elsholtz FHJ, Hamm B, Niehues SM. Can optimized model-based iterative reconstruction improve the contrast of liver lesions in CT? Acta Radiol 2023; 64:42-50. [PMID: 34985369 PMCID: PMC9780754 DOI: 10.1177/02841851211070119] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
BACKGROUND Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability. PURPOSE To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection. MATERIAL AND METHODS Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion. RESULTS A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRSTMILD showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRSTMILD; P < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRSTSTR showing the closest results (P < 0.001). CONCLUSION Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.
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Affiliation(s)
- Jonas Oppenheimer
- Department of Radiology, Charité, Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany ,Jonas Oppenheimer, Charité – Universitätsmedizin Berlin, Clinic for Radiology Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany.
| | - Keno Kyrill Bressem
- Department of Radiology, Charité, Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany ,Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Fabian Henry Jürgen Elsholtz
- Department of Radiology, Charité, Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité, Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stefan Markus Niehues
- Department of Radiology, Charité, Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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84
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Zhou Z, Gao Y, Zhang W, Bo K, Zhang N, Wang H, Wang R, Du Z, Firmin D, Yang G, Zhang H, Xu L. Artificial intelligence-based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study. Eur Radiol 2023; 33:678-689. [PMID: 35788754 DOI: 10.1007/s00330-022-08975-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/16/2022] [Accepted: 06/20/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm. METHODS We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets (n = 100) and the validation datasets (n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy. RESULTS The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K-values > 0.80, p < 0.05). CONCLUSIONS The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm. KEY POINTS • The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm. • Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images. • No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.
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Affiliation(s)
- Zhen Zhou
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Yifeng Gao
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Weiwei Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Kairui Bo
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Hui Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Rui Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Zhiqiang Du
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - David Firmin
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK.,National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China.
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85
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Systematic assessment of coronary calcium detectability and quantification on four generations of CT reconstruction techniques: a patient and phantom study. Int J Cardiovasc Imaging 2023; 39:221-231. [PMID: 36598691 DOI: 10.1007/s10554-022-02703-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/24/2022] [Indexed: 01/07/2023]
Abstract
In computed tomography, coronary artery calcium (CAC) scores are influenced by image reconstruction. The effect of a newly introduced deep learning-based reconstruction (DLR) on CAC scoring in relation to other algorithms is unknown. The aim of this study was to evaluate the effect of four generations of image reconstruction techniques (filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), and DLR) on CAC detectability, quantification, and risk classification. First, CAC detectability was assessed with a dedicated static phantom containing 100 small calcifications varying in size and density. Second, CAC quantification was assessed with a dynamic coronary phantom with velocities equivalent to heart rates of 60-75 bpm. Both phantoms were scanned and reconstructed with four techniques. Last, scans of fifty patients were included and the Agatston calcium score was calculated for all four reconstruction techniques. FBP was used as a reference. In the phantom studies, all reconstruction techniques resulted in less detected small calcifications, up to 22%. No clinically relevant quantification changes occurred with different reconstruction techniques (less than 10%). In the patient study, the cardiovascular risk classification resulted, for all reconstruction techniques, in excellent agreement with the reference (κ = 0.96-0.97). However, MBIR resulted in significantly higher Agatston scores (61 (5.5-435.0) vs. 81.5 (9.25-435.0); p < 0.001) and 6% reclassification rate. In conclusion, HIR and DLR reconstructed scans resulted in similar Agatston scores with excellent agreement and low-risk reclassification rate compared with routine reconstructed scans (FBP). However, caution should be taken with low Agatston scores, as based on phantom study, detectability of small calcifications varies with the used reconstruction algorithm, especially with MBIR and DLR.
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Kim SY, Suh YJ, Kim NY, Lee S, Nam K, Kim J, Kim H, Lee H, Han K, Yong HS. A Modified Length-Based Grading Method for Assessing Coronary Artery Calcium Severity on Non-Electrocardiogram-Gated Chest Computed Tomography: A Multiple-Observer Study. Korean J Radiol 2023; 24:284-293. [PMID: 36996903 PMCID: PMC10067688 DOI: 10.3348/kjr.2022.0826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/19/2022] [Accepted: 02/04/2023] [Indexed: 03/29/2023] Open
Abstract
OBJECTIVE To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT). MATERIALS AND METHODS This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen's kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods. RESULTS For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496-0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636-0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen's kappa, 0.565 [95% CI: 0.511-0.619 for visual assessment vs. 0.695 [95% CI: 0.638-0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) (P < 0.001). CONCLUSION The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment.
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Affiliation(s)
- Suh Young Kim
- Department of Radiology, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea
- Department of Medicine, Yonsei University Graduate School, College of Medicine, Seoul, Korea
| | - Young Joo Suh
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Na Young Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Suji Lee
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kyungsun Nam
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jeongyun Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hwan Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyunji Lee
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hwan Seok Yong
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
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Diagnostic Performance in Low- and High-Contrast Tasks of an Image-Based Denoising Algorithm Applied to Radiation Dose-Reduced Multiphase Abdominal CT Examinations. AJR Am J Roentgenol 2023; 220:73-85. [PMID: 35731096 DOI: 10.2214/ajr.22.27806] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND. Anatomic redundancy between phases can be used to achieve denoising of multiphase CT examinations. A limitation of iterative reconstruction (IR) techniques is that they generally require use of CT projection data. A frequency-split multi-band-filtration algorithm applies denoising to the multiphase CT images themselves. This method does not require knowledge of the acquisition process or integration into the reconstruction system of the scanner, and it can be implemented as a supplement to commercially available IR algorithms. OBJECTIVE. The purpose of the present study is to compare radiologists' performance for low-contrast and high-contrast diagnostic tasks (i.e., tasks for which differences in CT attenuation between the imaging target and its anatomic background are subtle or large, respectively) evaluated on multiphase abdominal CT between routine-dose images and radiation dose-reduced images processed by a frequency-split multiband-filtration denoising algorithm. METHODS. This retrospective single-center study included 47 patients who underwent multiphase contrast-enhanced CT for known or suspected liver metastases (a low-contrast task) and 45 patients who underwent multiphase contrast-enhanced CT for pancreatic cancer staging (a high-contrast task). Radiation dose-reduced images corresponding to dose reduction of 50% or more were created using a validated noise insertion technique and then underwent denoising using the frequency-split multi-band-filtration algorithm. Images were independently evaluated in multiple sessions by different groups of abdominal radiologists for each task (three readers in the low-contrast arm and four readers in the high-contrast arm). The noninferiority of denoised radiation dose-reduced images to routine-dose images was assessed using the jackknife alternative free-response ROC (JAFROC) figure-of-merit (FOM; limit of noninferiority, -0.10) for liver metastases detection and using the Cohen kappa statistic and reader confidence scores (100-point scale) for pancreatic cancer vascular invasion. RESULTS. For liver metastases detection, the JAFROC FOM for denoised radiation dose-reduced images was 0.644 (95% CI, 0.510-0.778), and that for routine-dose images was 0.668 (95% CI, 0.543-0.792; estimated difference, -0.024 [95% CI, -0.084 to 0.037]). Intraobserver agreement for pancreatic cancer vascular invasion was substantial to near perfect when the two image sets were compared (κ = 0.53-1.00); the 95% CIs of all differences in confidence scores between image sets contained zero. CONCLUSION. Multiphase contrast-enhanced abdominal CT images with a radiation dose reduction of 50% or greater that undergo denoising by a frequency-split multiband-filtration algorithm yield performance similar to that of routine-dose images for detection of liver metastases and vascular staging of pancreatic cancer. CLINICAL IMPACT. The image-based denoising algorithm facilitates radiation dose reduction of multiphase examinations for both low- and high-contrast diagnostic tasks without requiring manufacturer-specific hardware or software.
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88
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Jeon PH, Lee CL. Deep learning image reconstruction for quality assessment of iodine concentration in computed tomography: A phantom study. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:409-422. [PMID: 36744361 DOI: 10.3233/xst-221356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
BACKGROUND Recently, deep learning reconstruction (DLR) technology aiming to improve image quality with minimal radiation dose has been applied not only to pediatric scans, but also to computed tomography angiography (CTA). OBJECTIVE To evaluate image quality characteristics of filtered back projection (FBP), hybrid iterative reconstruction [Adaptive Iterative Dose Reduction 3D (AIDR 3D)], and DLR (AiCE) using different iodine concentrations and scan parameters. METHODS Phantoms with eight iodine concentrations (ranging from 1.2 to 25.9 mg/mL) located at the edge of a cylindrical water phantom with a diameter of 19 cm were scanned. Data were reconstructed with FBP, AIDR 3D, and AiCE using various scan parameters of tube current and voltage using a 320 row-detector CT scanner. Data obtained using different reconstruction techniques were quantitatively compared by analyzing Hounsfield units (HU), noise, and contrast-to-noise ratios (CNRs). RESULTS HU values of FBP and AIDR 3D were constant even when the iodine concentration was changed, whereas AiCE showed the highest HU value when the iodine concentration was low, but the HU value reversed when the iodine concentration exceeded a certain value. In the AIDR 3D and AiCE, the noise decreased as the tube current increased, and the change in noise when the iodine concentration was inconsistent. AIDR 3D and AiCE yielded better noise reduction rates than with FBP at a low tube current. The noise reduction rate of AIDR 3D and AiCE compared to that of FBP showed characteristics ranging from 7% to 35%, and the noise reduction rate of AiCE compared to that of AIDR 3D ranged from 2.0% to 13.3%. CONCLUSIONS The evaluated reconstruction techniques showed different image quality characteristics (HU value, noise, and CNR) according to dose and scan parameters, and users must consider these results and characteristics before performing patient scans.
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Affiliation(s)
- Pil-Hyun Jeon
- Department of Diagnostic Radiology, Yonsei University Wonju College of Medicine, Wonju Severance Christian Hospital, Wonju-Si, Gangwon-Do, Republic of Korea
| | - Chang-Lae Lee
- Health & Medical Equipment Business Unit, Samsung Electronics, Suwon-Si, Gyeonggi-Do, Republic of Korea
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89
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Jiang C, Jin D, Liu Z, Zhang Y, Ni M, Yuan H. Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance. Insights Imaging 2022; 13:182. [DOI: 10.1186/s13244-022-01308-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/24/2022] [Indexed: 11/28/2022] Open
Abstract
Abstract
Objectives
To evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V).
Methods
Carotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups.
Results
The image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000–0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000–0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000–0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05).
Conclusions
Compared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction.
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90
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Hee Kim K, Choo KS, Jin Nam K, Lee K, Hwang JY, Park C, Jung Yang W. Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction "TrueFidelity" in children with congenital heart disease. Medicine (Baltimore) 2022; 101:e31169. [PMID: 36281124 PMCID: PMC9592454 DOI: 10.1097/md.0000000000031169] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Several recent studies have reported that deep learning reconstruction "TrueFidelity" (TF) improves computed tomography (CT) image quality. However, no study has compared adaptive statistical repeated reconstruction (ASIR-V) using TF in pediatric cardiac CT angiography (CTA) with a low peak kilovoltage. OBJECTIVE This study aimed to determine whether ASIR-V or TF CTA image quality is superior in children with congenital heart disease (CHD). MATERIALS AND METHODS Fifty children (median age, 2 months; interquartile range, 0-5 months; 28 men) with CHD who underwent CTA were enrolled between June and September 2020. Images were reconstructed using 2 ASIR-V blending factors (80% and 100% [AV-100]) and 3 TF settings (low, medium, and high [TF-H] strength levels). For the quantitative analyses, 3 objective image qualities (attenuation, noise, and signal-to-noise ratio [SNR]) were measured of the great vessels and heart chambers. The contrast-to-noise ratio (CNR) was also evaluated between the left ventricle and the dial wall. For the qualitative analyses, the degree of quantum mottle and blurring at the upper level to the first branch of the main pulmonary artery was assessed independently by 2 radiologists. RESULTS When the ASIR-V blending factor level and TF strength were higher, the noise was lower, and the SNR was higher. The image noise and SNR of TF-H were significantly lower and higher than those of AV-100 (P < .01), except for noise in the right atrium and left pulmonary artery and SNR of the right ventricle. Regarding CNR, TF-H was significantly better than AV-100 (P < .01). In addition, in the objective assessment of the degree of quantum mottle and blurring, TF-H had the best score among all examined image sets (P < .01). CONCLUSION TF-H is superior to AV-100 in terms of objective and subjective image quality. Consequently, TF-H was the best image set for cardiac CTA in children with CHD.
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Affiliation(s)
- Kun Hee Kim
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - Ki Seok Choo
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
- *Correspondence: Ki Seok Choo, Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Beomeo-RI, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do, 626-770, Korea (e-mail: )
| | - Kyoung Jin Nam
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - Kyeyoung Lee
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - Jae-Yeon Hwang
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - ChanKue Park
- Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si, Gyeongsangnam-do, Korea
| | - Woo Jung Yang
- Barunmom Rehabilitation Medicine, Busanjin-gu, Busan, Korea
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The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images. Diagnostics (Basel) 2022; 12:diagnostics12102560. [PMID: 36292249 PMCID: PMC9601258 DOI: 10.3390/diagnostics12102560] [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: 08/28/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022] Open
Abstract
This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians’ diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening.
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92
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Zhao K, Jiang B, Zhang S, Zhang L, Zhang L, Feng Y, Li J, Zhang Y, Xie X. Measurement Accuracy and Repeatability of RECIST-Defined Pulmonary Lesions and Lymph Nodes in Ultra-Low-Dose CT Based on Deep Learning Image Reconstruction. Cancers (Basel) 2022; 14:5016. [PMID: 36291800 PMCID: PMC9599467 DOI: 10.3390/cancers14205016] [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: 07/25/2022] [Accepted: 10/11/2022] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND Deep learning image reconstruction (DLIR) improves image quality. We aimed to compare the measured diameter of pulmonary lesions and lymph nodes between DLIR-based ultra-low-dose CT (ULDCT) and contrast-enhanced CT. METHODS The consecutive adult patients with noncontrast chest ULDCT (0.07-0.14 mSv) and contrast-enhanced CT (2.38 mSv) were prospectively enrolled. Patients with poor image quality and body mass index ≥ 30 kg/m2 were excluded. The diameter of pulmonary target lesions and lymph nodes defined by Response Evaluation Criteria in Solid Tumors (RECIST) was measured. The measurement variability between ULDCT and enhanced CT was evaluated by Bland-Altman analysis. RESULTS The 141 enrolled patients (62 ± 12 years) had 89 RECIST-defined measurable pulmonary target lesions (including 30 malignant lesions, mainly adenocarcinomas) and 45 measurable mediastinal lymph nodes (12 malignant). The measurement variation of pulmonary lesions between high-strength DLIR (DLIR-H) images of ULDCT and contrast-enhanced CT was 2.2% (95% CI: 1.7% to 2.6%) and the variation of lymph nodes was 1.4% (1.0% to 1.9%). CONCLUSIONS The measured diameters of pulmonary lesions and lymph nodes in DLIR-H images of ULDCT are highly close to those of contrast-enhanced CT. DLIR-based ULDCT may facilitate evaluating target lesions with greatly reduced radiation exposure in tumor evaluation and lung cancer screening.
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Affiliation(s)
- Keke Zhao
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Beibei Jiang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Shuai Zhang
- CT Imaging Research Center, GE Healthcare China, Shanghai 201203, China
| | - Lu Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Lin Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Yan Feng
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Jianying Li
- CT Imaging Research Center, GE Healthcare China, Shanghai 201203, China
| | - Yaping Zhang
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
| | - Xueqian Xie
- Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China
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TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research. Phys Med 2022; 102:33-45. [PMID: 36088800 DOI: 10.1016/j.ejmp.2022.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/08/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022] Open
Abstract
We presented TIGRE-VarianCBCT, an open-source toolkit Matlab-GPU for Varian on-board cone-beam CT with particular emphasis to address challenges in raw data preprocessing, artifacts correction, tomographic reconstruction and image post-processing. The aim of this project is to provide not only a tool to bridge the gap between clinical usage of CBCT scan data and research algorithms but also a framework that breaks down the imaging chain into individual processes so that research effort can be focused on a specific part. The entire imaging chain, module-based architecture, data flow and techniques used in the creation of the toolkit are presented. Raw scan data are first decoded to extract X-ray fluoro image series and set up the imaging geometry. Data conditioning operations including scatter correction, normalization, beam-hardening correction, ring removal are performed sequentially. Reconstruction is supported by TIGRE with FDK as well as a variety of iterative algorithms. Pixel-to-HU mapping is calibrated by a CatphanTM 504 phantom. Imaging dose in CTDIw is calculated in an empirical formula. The performance was validated on real patient scans with good agreement with respect to vendor-designed program. Case studies in scan protocol optimization, low dose imaging and iterative algorithm comparison demonstrated its substantial potential in performing scan data based clinical studies. The toolkit is released under the BSD license, imposing minimal restrictions on its use and distribution. The toolkit is accessible as a module at https://github.com/CERN/TIGRE.
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Shigematsu S, Oda S, Sakabe D, Matsuoka A, Hayashi H, Taguchi N, Kidoh M, Nagayama Y, Nakaura T, Murakami M, Hatemura M, Hirai T. Practical Preventive Strategies for Extravasation of Contrast Media During CT: What the Radiology Team Should Do. Acad Radiol 2022; 29:1555-1559. [PMID: 35246376 DOI: 10.1016/j.acra.2022.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/05/2022] [Accepted: 01/09/2022] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES This study aimed to assess the effectiveness of practical preventive strategies (i.e., venous vulnerability assessment and prevention scan protocol rules) taken by our radiology team (radiology nurses, radiology technicians, radiologists) on reducing extravasation of contrast media (ECM) during CT. MATERIALS AND METHODS A total of 73,931 patients who underwent contrast-enhanced CT scans between January 2013 and December 2019 were retrospectively included. Venous vulnerability assessment by the radiology team began in 2015, and prevention scan protocol rules for the prevention of ECM were added in 2017. We defined each period as follows: 2013-2014, no prevention (Period A); 2015-2016, early prevention (Period B, venous vulnerability assessment only); and 2017-2019: late prevention (Period C, venous vulnerability assessment with prevention scan protocol rules). The incident reports, radiology reports, and medical records of patients in whom ECM occurred were reviewed. We compared the frequency of ECM during each period. RESULTS ECM occurred in 0.39% (292/73,931) of the patients. The frequencies of ECM for Periods A, B, and C were 0.62% (121/19,505), 0.43% (89/20,847), and 0.24% (82/33,579), respectively. There were significant differences in the frequencies of ECM among the three periods (Chi-squared test, p < 0.01). CONCLUSION Implementation of venous vulnerability assessment and prevention scan protocol rules by a radiology team can be a practical and simple solution to reduce the risk of ECM during CT.
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Affiliation(s)
- Shinsuke Shigematsu
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan.
| | - Daisuke Sakabe
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Ayumi Matsuoka
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Hidetaka Hayashi
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Narumi Taguchi
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Michiyo Murakami
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Masahiro Hatemura
- Department of Central Radiology, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto, 860-8556, Japan
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Giovannetti G, Guerrini A, Minozzi S, Panetta D, Salvadori PA. Computer tomography and magnetic resonance for multimodal imaging of fossils and mummies. Magn Reson Imaging 2022; 94:7-17. [PMID: 36084902 DOI: 10.1016/j.mri.2022.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/26/2022] [Accepted: 08/31/2022] [Indexed: 11/24/2022]
Abstract
The study of fossils and mummies has largely benefited from the use of modern noninvasive and nondestructive imaging technologies and represents a fast developing area. In this review, we describe the emerging role of imaging based on Magnetic Resonance (MR) and Computer Tomography (CT) employed for the study of ancient remains and mummies. For each methodology, the state of the art in paleoradiology applications is described, by emphasizing new technologies developed in the field of both CT, such as micro- and nano-CT, dual-energy and multi-energy CT, and MR, with the description of novel dedicated sequences, radiofrequency coils and gradients. The complementarity of CT and MR in paleoradiology is also discussed, by pointing out what MR provides in addition to CT, with an overview on the state of the art of emerging strategies in the use of CT/MR combination for the study of a sample following a multimodal integrated approach.
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Affiliation(s)
- Giulio Giovannetti
- Institute of Clinical Physiology, National Council of Research, via G. Moruzzi 1, 56124 Pisa, Italy.
| | - Andrea Guerrini
- Gruppo Archeologico e Paleontologico Livornese, Museo di Storia Naturale del Mediterraneo, via Roma, 234, 57127 Leghorn, Italy
| | - Simona Minozzi
- Division of Paleopathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 57, 56100 Pisa, Italy
| | - Daniele Panetta
- Institute of Clinical Physiology, National Council of Research, via G. Moruzzi 1, 56124 Pisa, Italy
| | - Piero A Salvadori
- Institute of Clinical Physiology, National Council of Research, via G. Moruzzi 1, 56124 Pisa, Italy
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Improved Single Breath-Hold SSFSE Sequence for Liver MRI Based on Compressed Sensing: Evaluation of Image Quality Compared with Conventional T2-Weighted Sequences. Diagnostics (Basel) 2022; 12:diagnostics12092164. [PMID: 36140565 PMCID: PMC9497881 DOI: 10.3390/diagnostics12092164] [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: 07/07/2022] [Revised: 09/03/2022] [Accepted: 09/04/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of this study was to evaluate the image quality of compressed-sensing accelerated single-shot fast spin-echo (SSFSECS) sequences acquired within a single breath-hold in comparison with conventional SSFSE (SSFSECONV) and multishot TSE (mTSE). A total of 101 patients who underwent liver MRI at 3 T, including SSFSECONV (acquisition time (TA) = 58−62 s), mTSE (TA = 108 s), and SSFSECS (TA = 18 s), were included in this retrospective study. Two radiologists assessed the three sequences with respect to artifacts, organ sharpness, small structure visibility, overall image quality, and conspicuity of main lesions of liver and pancreas using a five-point evaluation scale system. Descriptive statistics and the Wilcoxon signed-rank test were used for statistical analysis. SSFSECS was significantly better than SSFSECONV and mTSE for artifacts, small structure visibility, overall image quality, and conspicuity of main lesions (p < 0.005). Regarding organ sharpness, mTSE and SSFSECS did not significantly differ (p = 0.554). Conspicuity of liver lesion did not significantly differ between SSFSECONV and mTSE (p = 0.404). SSFSECS showed superior image quality compared with SSFSECONV and mTSE despite a more than three-fold reduction in TA, suggesting a remarkable potential for saving time in liver imaging.
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97
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A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice. CURRENT RADIOLOGY REPORTS 2022. [DOI: 10.1007/s40134-022-00399-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Abstract
Purpose of Review
Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. This review summarizes the prior reconstruction methods, introduces DLR, and then reviews recent findings from DLR from a physics and clinical perspective.
Recent Findings
DLR has been shown to allow for noise magnitude reductions relative to filtered back-projection without suffering from “plastic” or “blotchy” noise texture that was found objectionable with most iterative and model-based solutions. Clinically, early reader studies have reported increases in subjective quality scores and studies have successfully implemented DLR-enabled dose reductions.
Summary
The future of CT image reconstruction is bright; deep learning methods have only started to tackle problems in this space via addressing noise reduction. Artifact mitigation and spectral applications likely be future candidates for DLR applications.
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98
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Shah R, Elangovan A, Jordan DW, Katz J, Cooper GS. 10-Year Trend of Abdominal Magnetic Resonance Imaging Compared With Abdominal Computed Tomography Scans in Inflammatory Bowel Disease. Inflamm Bowel Dis 2022; 28:1357-1362. [PMID: 34935946 DOI: 10.1093/ibd/izab284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Indexed: 12/09/2022]
Abstract
BACKGROUND Patients with inflammatory bowel disease (IBD) frequently undergo multiple computed tomography (CT) examinations. With the widespread availability of magnetic resonance imaging (MRI), it is unclear whether the use of CTs in IBD has declined. We aimed to analyze the trends of CT and MRI use in a large cohort of IBD patients in a 10-year period. METHODS We retrospectively analyzed adults ≥18 years of age using a de-identified database, IBM Explorys. Patients with ≥1 CT of the abdomen (± pelvis) or MRI of the abdomen (± pelvis) at least 30 days after the diagnosis of Crohn's disease (CD) or ulcerative colitis (UC) were included. We examined the factors associated with patients undergoing multiple CTs (≥5 CTs of the abdomen) and performed a trend analysis from 2010 to 2019. RESULTS Among 176 110 CD and 143 460 UC patients, those with ≥1 CT of the abdomen annually increased from 2010 to 2019 with mean annual percentage change of +3.6% for CD and +4.9% for UC. Similarly, annual percentage change for patients with ≥1 MRI (CD: +15.6%; UC: +22.8%) showed a rising trend. There was a 3.8% increase in CD patients receiving ≥5 CTs of the abdomen annually compared with a 2.4% increase among UC patients in the 10-year period. Age ≥50 years, men, African Americans, public insurance payors, body mass index ≥30kg/m2, and smoking history were associated with ≥5 CTs. CONCLUSIONS There is a considerable increase in the number of CT scans performed in IBD patients. Further studies can explore factors influencing the use of CT and MRI of the abdomen in IBD patients.
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Affiliation(s)
- Raj Shah
- Division of Gastroenterology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA.,Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Boston, MA, USA
| | - Abbinaya Elangovan
- Division of Gastroenterology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - David W Jordan
- Department of Radiology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Jeffry Katz
- Division of Gastroenterology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Gregory S Cooper
- Division of Gastroenterology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
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Kawashima H, Ichikawa K, Takata T, Seto I. Comparative Assessment of Noise Properties for Two Deep Learning CT Image Reconstruction Techniques and Filtered Back Projection. Med Phys 2022; 49:6359-6367. [PMID: 36047991 DOI: 10.1002/mp.15918] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/25/2022] [Accepted: 08/03/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Two deep-learning image reconstruction (DLIR) techniques from two different CT vendors have recently been introduced into clinical practice. PURPOSE To characterize the noise properties of two DLIR techniques with different training methods, using a phantom containing a simple uniform and a complex non-uniform region. METHODS A water-bath phantom with a diameter of 300 mm was used as a base phantom. A textured phantom with a diameter of 128 mm, which was made of two materials, one equivalent to water and the other being 12 mg/mL diluted iodine, irregularly mixed to create a complex texture (non-uniform region), was placed in the base phantom. Thirty repeated phantom scans were performed using two CT scanners (GE, Revolution CT with Apex Edition; Canon, Aquilion One PRISM Edition) at two dose levels (CTDI: 5 and 15 mGy). Images were reconstructed with each CT system's filtered back projection (FBP) and DLIR [GE, TrueFidelity (TF); Canon, Advanced intelligent Clear-IQ Engine Body Sharp (AC)] for three process strengths. For basic characteristics of noise, the standard deviation (SD) and noise power spectrum (NPS) were measured for the uniform (water) region. A noise magnitude map was generated by calculating the inter-image SD at each pixel position across the 30 images. Then, a noise reduction map (NRM), which visualizes the relative differences in noise magnitude between FBP and DLIR, was calculated. The NRM values ranged from 0.0 to 1.0. A low NRM value represents a less aggressive noise reduction. The histograms of the NRM value were analyzed for the uniform and non-uniform regions. RESULTS The reduction in noise magnitude compared with FBP tended to be greater with AC (45%-85%) than with TF (32%-65%). The average NPS frequencies of TF and AC were almost comparable to those of FBP, except for the low-dose condition and the high noise reduction strength for AC. The NRM values of TF and AC were higher in the uniform region than in the non-uniform region. In the non-uniform region, TF's average NRM values (0.21-0.48) tended to be lower than AC's (0.39-0.78). The histograms for TF showed a small overlap between the uniform and the non-uniform regions; in contrast, those for AC showed a greater overlap. This difference seems to indicate that TF processes the uniform and non-uniform regions more differently than AC does. CONCLUSION This study has revealed a distinct difference in characteristics between the two DLIR techniques: TF tends to offer less aggressive noise reduction in non-uniform regions and preserve the original signals, whereas AC tends to prioritize noise filtering over edge-preservation, especially at the low-dose condition and with the high noise reduction strength. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University
| | - Katsuhiro Ichikawa
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University
| | | | - Issei Seto
- Department of Radiological Technology, Mitsubishi Kyoto Hospital
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100
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Alsleem H, Tajaldeen A, Almutairi A, Almohiy H, Aldaais E, Albattat R, Alsleem M, Abuelhia E, Kheiralla OAM, Alqahtani A, Alghamdi S, Aljondi R, Alharbi R. The Actual Role of Iterative Reconstruction Algorithm Methods in Several Saudi Hospitals As A Tool For Radiation Dose Minimization of Ct Scan Examinations. J Multidiscip Healthc 2022; 15:1747-1757. [PMID: 36016857 PMCID: PMC9398457 DOI: 10.2147/jmdh.s376729] [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: 06/13/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022] Open
Abstract
Background Iterative reconstruction algorithm (IR) techniques were developed to maintain a lower radiation dose for patients as much as possible while achieving the required image quality and medical benefits. The main purpose of the current research was to assess the level and usage extent of IR techniques in computed tomographic (CT) scan exams. Also, the obligation of practitioners in several hospitals in Saudi Arabia to implement IR in CT exams was assessed. Material and Methodology The recent research was based on two studies: data collection and a survey study. Data on the CT scan examinations were retrospectively collected from CT scanners. The survey was conducted using a questionnaire to evaluate radiographers’ and radiologists’ perceptions about IR and their practices with IR techniques. The statistical analysis results were performed to measure the usage strength level of IR methods. Results and Discussions The IR strength level of 50% was selected for nearly 80% of different CT examinations and patients of different ages and weights. About 46% of the participants had not learned about IR methods during their college studies, and 54% had not received formal training in applying IR techniques. Only 32% of the participants had adequate experience with IR. Half of the participants were not involved in the updating process of the CT protocol. Conclusion The results indicate that the majority of radiographer and radiologist at four different hospitals in Saudi Arabia have no explicit or understandable knowledge of selecting IR strength levels during the CT examination of patients. There is a need for more training in IR applications for both radiologists and radiographers. Training sessions were suggested to support radiographers and radiologists to efficiently utilize IR techniques to optimize image quality. Further studies are required to adjust CT exam protocols effectively to utilize the IR technique.
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Affiliation(s)
- Haney Alsleem
- Department of Radiological Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Abdulrahman Tajaldeen
- Department of Radiological Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | | | - Hussain Almohiy
- Radiological Sciences, King Khalid University, Abha, Saudi Arabia
| | - Ebtisam Aldaais
- Department of Radiological Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Rayan Albattat
- Medical Imaging Department, King Fahad Specialist Hospital, Dammam, Saudi Arabia
| | - Mousa Alsleem
- College of Dentistry, King Faisal University, Alahsa, Saudi Arabia
| | - Elfatih Abuelhia
- Department of Radiological Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | | | - Ahmed Alqahtani
- Radiology Department, King Saud Medical City, Riyadh, Saudi Arabia
| | - Salem Alghamdi
- Department of Applied Radiologic Technology, University of Jeddah, Jeddah, Saudi Arabia
| | - Rowa Aljondi
- Department of Applied Radiologic Technology, University of Jeddah, Jeddah, Saudi Arabia
| | - Renad Alharbi
- Department of Radiology, Specialized Medical Complex, Jeddah, Saudi Arabia
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