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Fujima N, Nakagawa J, Ikebe Y, Kameda H, Harada T, Shimizu Y, Tsushima N, Kano S, Homma A, Kwon J, Yoneyama M, Kudo K. Improved image quality in contrast-enhanced 3D-T1 weighted sequence by compressed sensing-based deep-learning reconstruction for the evaluation of head and neck. Magn Reson Imaging 2024; 108:111-115. [PMID: 38340971 DOI: 10.1016/j.mri.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/12/2024]
Abstract
PURPOSE To assess the utility of deep learning (DL)-based image reconstruction with the combination of compressed sensing (CS) denoising cycle by comparing images reconstructed by conventional CS-based method without DL in fat-suppressed (Fs)-contrast enhanced (CE) three-dimensional (3D) T1-weighted images (T1WIs) of the head and neck. MATERIALS AND METHODS We retrospectively analyzed the cases of 39 patients who had undergone head and neck Fs-CE 3D T1WI applying reconstructions based on conventional CS and CS augmented by DL, respectively. In the qualitative assessment, we evaluated overall image quality, visualization of anatomical structures, degree of artifacts, lesion conspicuity, and lesion edge sharpness based on a five-point system. In the quantitative assessment, we calculated the signal-to-noise ratios (SNRs) of the lesion and the posterior neck muscle and the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. RESULTS For all items of the qualitative analysis, significantly higher scores were awarded to images with DL-based reconstruction (p < 0.001). In the quantitative analysis, DL-based reconstruction resulted in significantly higher values for both the SNR of lesions (p < 0.001) and posterior neck muscles (p < 0.001). Significantly higher CNRs were also observed in images with DL-based reconstruction (p < 0.001). CONCLUSION DL-based image reconstruction integrating into the CS-based denoising cycle offered superior image quality compared to the conventional CS method. This technique will be useful for the assessment of patients with head and neck disease.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan.
| | - Junichi Nakagawa
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Yohei Ikebe
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan; Center for Cause of Death investigation, Faculty of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan
| | - Hiroyuki Kameda
- Faculty of Dental Medicine Department of Radiology Hokkaido University, N13 W7, Kita-ku, Sapporo, Hokkaido 060-8586, Japan
| | - Taisuke Harada
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan
| | - Nayuta Tsushima
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita ku, Sapporo 060-8638, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita ku, Sapporo 060-8638, Japan
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita ku, Sapporo 060-8638, Japan
| | - Jihun Kwon
- Philips Japan, 3-37 Kohnan 2-chome, Minato-ku, Tokyo 108-8507, Japan
| | - Masami Yoneyama
- Philips Japan, 3-37 Kohnan 2-chome, Minato-ku, Tokyo 108-8507, Japan
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 0608638, Japan; Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan; Clinical AI Human Resources Development Program, Faculty of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan
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Brem O, Elisha D, Konen E, Amitai M, Klang E. Deep learning in magnetic resonance enterography for Crohn's disease assessment: a systematic review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04326-4. [PMID: 38693270 DOI: 10.1007/s00261-024-04326-4] [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: 03/15/2024] [Revised: 03/15/2024] [Accepted: 04/01/2024] [Indexed: 05/03/2024]
Abstract
Crohn's disease (CD) poses significant morbidity, underscoring the need for effective, non-invasive inflammatory assessment using magnetic resonance enterography (MRE). This literature review evaluates recent publications on the role of deep learning in improving MRE for CD assessment. We searched MEDLINE/PUBMED for studies that reported the use of deep learning algorithms for assessment of CD activity. The study was conducted according to the PRISMA guidelines. The risk of bias was evaluated using the QUADAS-2 tool. Five eligible studies, encompassing 468 subjects, were identified. Our study suggests that diverse deep learning applications, including image quality enhancement, bowel segmentation for disease burden quantification, and 3D reconstruction for surgical planning are useful and promising for CD assessment. However, most of the studies are preliminary, retrospective studies, and have a high risk of bias in at least one category. Future research is needed to assess how deep learning can impact CD patient diagnostics, particularly when considering the increasing integration of such models into hospital systems.
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Affiliation(s)
- Ofir Brem
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
- Arrow Program for Research Education, Sheba Medical Center, Tel-Hashomer, Israel.
| | - David Elisha
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Arrow Program for Research Education, Sheba Medical Center, Tel-Hashomer, Israel
| | - Eli Konen
- Division of Diagnostic Imaging, The Chaim Sheba Medical Center, Affiliated to the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Michal Amitai
- Arrow Program for Research Education, Sheba Medical Center, Tel-Hashomer, Israel
- Division of Diagnostic Imaging, The Chaim Sheba Medical Center, Affiliated to the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Klang
- Arrow Program for Research Education, Sheba Medical Center, Tel-Hashomer, Israel
- The Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Kim JH, Yoon JH, Kim SW, Park J, Bae SH, Lee JM. Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:738-747. [PMID: 38095685 DOI: 10.1007/s00261-023-04124-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/30/2023] [Accepted: 11/03/2023] [Indexed: 03/05/2024]
Abstract
PURPOSE To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) in patients at a high risk of hepatocellular carcinoma. MATERIALS AND METHODS In this retrospective analysis, 83 high-risk patients with hepatocellular carcinoma underwent gadoxetic acid-enhanced liver MRI using a 3-T scanner. Triple arterial phase, high-resolution portal venous phase, and high-resolution hepatobiliary phase images were reconstructed using conventional reconstruction techniques and DLRA (AIRTM Recon DL; GE Healthcare) for subsequent comparison. Image quality and solid focal lesion detection were assessed by three abdominal radiologists and compared between conventional and DL methods. Focal liver lesion detection was evaluated using figures of merit (FOMs) from a jackknife alternative free-response receiver operating characteristic analysis on a per-lesion basis. RESULTS DLRA-reconstructed images exhibited significantly improved overall image quality, image contrast, lesion conspicuity, vessel conspicuity, and liver edge sharpness and reduced subjective image noise, ringing artifacts, and motion artifacts compared to conventionally reconstructed images (all P < 0.05). Although there was no significant difference in the FOMs of non-cystic focal liver lesions between the conventional and DL methods, DLRA-reconstructed images showed notably higher pooled sensitivity than conventionally reconstructed images (P < 0.05) in all phases and higher detection rates for viable post-treatment HCCs in the arterial and hepatobiliary phases (all P < 0.05). CONCLUSIONS Implementing DLRA can enhance the image quality in 3D T1-weighted gradient-echo sequences of gadoxetic acid-enhanced liver MRI examinations, leading to improved detection of viable post-treatment HCCs.
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Affiliation(s)
- Jae Hyun Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea
| | - Se Woo Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea
| | - Junghoan Park
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea
| | - Seong Hwan Bae
- Department of Radiology, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
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Lee Y, Yoon S, Park SH, Nickel MD. Advanced Abdominal MRI Techniques and Problem-Solving Strategies. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2024; 85:345-362. [PMID: 38617869 PMCID: PMC11009130 DOI: 10.3348/jksr.2023.0067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/14/2023] [Indexed: 04/16/2024]
Abstract
MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI. We aimed to present various advanced abdominal MRI techniques for reducing the scan time while maintaining image quality and free-breathing techniques for dynamic imaging and illustrate cases using the techniques mentioned above. A review of these advanced techniques can assist in the appropriate interpretation of sequences.
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Park EJ, Lee Y, Lee HJ, Son JH, Yi J, Hahn S, Lee J. Impact of deep learning-based reconstruction and anti-peristaltic agent on the image quality and diagnostic performance of magnetic resonance enterography comparing single breath-hold single-shot fast spin echo with and without anti-peristaltic agent. Quant Imaging Med Surg 2024; 14:722-735. [PMID: 38223037 PMCID: PMC10784037 DOI: 10.21037/qims-23-738] [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: 05/29/2023] [Accepted: 10/25/2023] [Indexed: 01/16/2024]
Abstract
Background While anti-peristaltic agents are beneficial for high quality magnetic resonance enterography (MRE), their use is constrained by potential side effects and increased examination complexity. We explored the potential of deep learning-based reconstruction (DLR) to compensate for the absence of anti-peristaltic agent, improve image quality and reduce artifact. This study aimed to evaluate the need for an anti-peristaltic agent in single breath-hold single-shot fast spin-echo (SSFSE) MRE and compare the image quality and artifacts between conventional reconstruction (CR) and DLR. Methods We included 45 patients who underwent MRE for Crohn's disease between October 2021 and September 2022. Coronal SSFSE images without fat saturation were acquired before and after anti-peristaltic agent administration. Four sets of data were generated: SSFSE CR with and without an anti-peristaltic agent (CR-A and CR-NA, respectively) and SSFSE DLR with and without an anti-peristaltic agent (DLR-A and DLR-NA, respectively). Two radiologists independently reviewed the images for overall quality and artifacts, and compared the three images with DLR-A. The degree of distension and inflammatory parameters were scored on a 5-point scale in the jejunum and ileum, respectively. Signal-to-noise ratio (SNR) levels were calculated in superior mesenteric artery (SMA) and iliac bifurcation level. Results In terms of overall quality, DLR-NA demonstrated no significant difference compared to DLR-A, whereas CR-NA and CR-A demonstrated significant differences (P<0.05, both readers). Regarding overall artifacts, reader 1 rated DLR-A slightly better than DLR-NA in four cases and rated them as identical in 41 cases (P=0.046), whereas reader 2 demonstrated no difference. Bowel distension was significantly different in the jejunum (Reader 1: P=0.046; Reader 2: P=0.008) but not in the ileum. Agreements between the images (Reader 1: ĸ=0.73-1.00; Reader 2: ĸ=1.00) and readers (ĸ=0.66 for all comparisons) on inflammation were considered good to excellent. The sensitivity, specificity, and accuracy in diagnosing inflammation in the terminal ileum were the same among DLR-NA, DLR-A, CR-NA and CR-A (94.42%, 81.83%, and 89.69 %; and 83.33%, 90.91%, and 86.21% for Readers 1 and 2, respectively). In both SMA and iliac bifurcation levels, SNR of DLR images exhibited no significant differences. CR images showed significantly lower SNR compared with DLR images (P<0.001). Conclusions SSFSE without anti-peristaltic agents demonstrated nearly equivalent quality to that with anti-peristaltic agents. Omitting anti-peristaltic agents before SSFSE and adding DLR could improve the scanning outcomes and reduce time.
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Affiliation(s)
- Eun Joo Park
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Yedaun Lee
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Jung Hee Son
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Jisook Yi
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Seok Hahn
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
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