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Nishioka N, Fujima N, Tsuneta S, Yoshikawa M, Kimura R, Sakamoto K, Kato F, Miyata H, Kikuchi H, Matsumoto R, Abe T, Kwon J, Yoneyama M, Kudo K. Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction. Eur J Radiol Open 2024; 13:100588. [PMID: 39070063 PMCID: PMC11276920 DOI: 10.1016/j.ejro.2024.100588] [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: 05/13/2024] [Revised: 06/16/2024] [Accepted: 06/30/2024] [Indexed: 07/30/2024] Open
Abstract
Purpose To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI). Methods This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue. Results In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p<0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p<0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p<0.0001, p=0.0014, respectively). Conclusion Model-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research.
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Affiliation(s)
- Noriko Nishioka
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan
| | - Satonori Tsuneta
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan
- Department of Radiology, Graduate School of Dental Medicine, Hokkaido University, N13 W7, Kita-ku, Sapporo 060-8586, Japan
| | - Masato Yoshikawa
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan
| | - Rina Kimura
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
| | - Keita Sakamoto
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan
| | - Fumi Kato
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan
- Department of Radiology, Jichi Medical University Saitama Medical Center, 1-847 Amanuma-cho, Omiya-ku, Saitama, 330-8503, Japan
| | - Haruka Miyata
- Department of Renal and Genitourinary Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo 060-8638, Japan
| | - Hiroshi Kikuchi
- Department of Renal and Genitourinary Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo 060-8638, Japan
| | - Ryuji Matsumoto
- Department of Renal and Genitourinary Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo 060-8638, Japan
| | - Takashige Abe
- Department of Renal and Genitourinary Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo 060-8638, Japan
| | - Jihun Kwon
- Philips Japan Ltd., 1-3-1 Azabudai, Minato-ku, Tokyo 106-0041, Japan
| | - Masami Yoneyama
- Philips Japan Ltd., 1-3-1 Azabudai, Minato-ku, Tokyo 106-0041, Japan
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, 060-8648, Japan
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Lee Y, Yoon S, Paek M, Han D, Choi MH, Park SH. Advanced MRI techniques in abdominal imaging. Abdom Radiol (NY) 2024; 49:3615-3636. [PMID: 38802629 DOI: 10.1007/s00261-024-04369-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/29/2024]
Abstract
Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients' breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques.
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Affiliation(s)
- Yoonhee Lee
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Sungjin Yoon
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | | | - Dongyeob Han
- Siemens Healthineers Ltd, Seoul, Republic of Korea
| | - Moon Hyung Choi
- Department of Radiology, Catholic University of Korea Eunpyeong St Mary's Hospital, Seoul, Republic of Korea
| | - So Hyun Park
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea.
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Sauer ST, Christner SA, Lois AM, Woznicki P, Curtaz C, Kunz AS, Weiland E, Benkert T, Bley TA, Baeßler B, Grunz JP. Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI. J Magn Reson Imaging 2024; 60:1190-1200. [PMID: 37974498 DOI: 10.1002/jmri.29139] [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: 07/14/2023] [Revised: 11/03/2023] [Accepted: 11/04/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising. PURPOSE To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI. STUDY TYPE Retrospective. POPULATION 133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI. FIELD STRENGTH/SEQUENCE 3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2). ASSESSMENT DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale. STATISTICAL TESTS Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant. RESULTS Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error. DATA CONCLUSION Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Stephanie Tina Sauer
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Sara Aniki Christner
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Anna-Maria Lois
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Piotr Woznicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Carolin Curtaz
- Department of Obstetrics and Gynecology, University Hospital Würzburg, Würzburg, Germany
| | - Andreas Steven Kunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thorsten Alexander Bley
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Jan-Peter Grunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
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Fujima N, Nakagawa J, Kameda H, Ikebe Y, Harada T, Shimizu Y, Tsushima N, Kano S, Homma A, Kwon J, Yoneyama M, Kudo K. Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck. MAGMA (NEW YORK, N.Y.) 2024; 37:439-447. [PMID: 37989922 DOI: 10.1007/s10334-023-01129-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023]
Abstract
OBJECTIVES To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI). MATERIALS AND METHODS We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. RESULTS Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001). DISCUSSION DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 060-8638, Japan.
| | - Junichi Nakagawa
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 060-8638, Japan
| | - Hiroyuki Kameda
- Faculty of Dental Medicine Department of Radiology, Hokkaido University, N13 W7, Kita-Ku, Sapporo, Hokkaido, 060-8586, 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
| | - Taisuke Harada
- Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 060-8638, 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 Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Medical AI Research and Development Center, Hokkaido University Hospital, N14 W5, 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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5
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Kim H, Choi MH, Lee YJ, Han D, Mostapha M, Nickel D. Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance. Acta Radiol 2024; 65:499-505. [PMID: 38343091 DOI: 10.1177/02841851241228192] [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: 05/25/2024]
Abstract
BACKGROUND The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. PURPOSE To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. METHODS This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. RESULTS In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. CONCLUSIONS DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.
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Affiliation(s)
- Hokun Kim
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Moon Hyung Choi
- Department of Radiology, Eunpyeong St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Young Joon Lee
- Department of Radiology, Eunpyeong St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dongyeob Han
- Research Collaboration, Siemens Healthineers Ltd, Seoul, Republic of Korea
| | - Mahmoud Mostapha
- Digital Technology & Innovation, Siemens Medical Solutions USA, Inc., Princeton, NJ, USA
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
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Ikeda H, Ohno Y, Yamamoto K, Murayama K, Ikedo M, Yui M, Kumazawa Y, Shimamura Y, Takagi Y, Nakagaki Y, Hanamatsu S, Obama Y, Ueda T, Nagata H, Ozawa Y, Iwase A, Toyama H. Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors. Cancers (Basel) 2024; 16:1714. [PMID: 38730665 PMCID: PMC11083776 DOI: 10.3390/cancers16091714] [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: 04/09/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning reconstruction (DLR) could be useful for image quality improvements. The purpose of this investigation using in vitro and in vivo studies was to determine the influence of sequence difference and of DLR for DWI on image quality, apparent diffusion coefficient (ADC) evaluation, and differentiation of malignant from benign head and neck tumors. METHODS For the in vitro study, a DWI phantom was scanned by FASE and EPI sequences and reconstructed with and without DLR. Each ADC within the phantom for each DWI was then assessed and correlated for each measured ADC and standard value by Spearman's rank correlation analysis. For the in vivo study, DWIs obtained by EPI and FASE sequences were also obtained for head and neck tumor patients. Signal-to-noise ratio (SNR) and ADC were then determined based on ROI measurements, while SNR of tumors and ADC were compared between all DWI data sets by means of Tukey's Honest Significant Difference test. RESULTS For the in vitro study, all correlations between measured ADC and standard reference were significant and excellent (0.92 ≤ ρ ≤ 0.99, p < 0.0001). For the in vivo study, the SNR of FASE with DLR was significantly higher than that of FASE without DLR (p = 0.02), while ADC values for benign and malignant tumors showed significant differences between each sequence with and without DLR (p < 0.05). CONCLUSION In comparison with EPI sequence, FASE sequence and DLR can improve image quality and distortion of DWIs without significantly influencing ADC measurements or differentiation capability of malignant from benign head and neck tumors.
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Affiliation(s)
- Hirotaka Ikeda
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Kaori Yamamoto
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Kazuhiro Murayama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Masato Ikedo
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Masao Yui
- Canon Medical Systems Corporation, Otawara 324-8550, Tochigi, Japan
| | - Yunosuke Kumazawa
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yurika Shimamura
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yui Takagi
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yuhei Nakagaki
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Satomu Hanamatsu
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yuki Obama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Takahiro Ueda
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Hiroyuki Nagata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Yoshiyuki Ozawa
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
| | - Akiyoshi Iwase
- Department of Radiology, Fujita Health University Hospital, Toyoake 470-1192, Aichi, Japan
| | - Hiroshi Toyama
- Department of Radiology, Fujita Health University School of Medicine, Toyoake 470-1192, Aichi, Japan
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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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Kim DK, Lee SY, Lee J, Huh YJ, Lee S, Lee S, Jung JY, Lee HS, Benkert T, Park SH. Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI. Magn Reson Imaging 2024; 105:82-91. [PMID: 37939970 DOI: 10.1016/j.mri.2023.11.003] [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: 08/08/2023] [Revised: 10/30/2023] [Accepted: 11/04/2023] [Indexed: 11/10/2023]
Abstract
PURPOSE To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI). METHOD This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI. RESULTS Ultimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55-0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P ≤ 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P ≤ 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16-0.89]). CONCLUSIONS DL reconstruction can improve the image quality of whole-spine diffusion imaging.
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Affiliation(s)
- Dong Kyun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Jinyoung Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yeon Jong Huh
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seungeun Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sungwon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyun-Soo Lee
- MR research Collaboration, Siemens Healthineers Ltd, Seoul, Republic of Korea
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Sung-Hong Park
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
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Wilpert C, Neubauer C, Rau A, Schneider H, Benkert T, Weiland E, Strecker R, Reisert M, Benndorf M, Weiss J, Bamberg F, Windfuhr-Blum M, Neubauer J. Accelerated Diffusion-Weighted Imaging in 3 T Breast MRI Using a Deep Learning Reconstruction Algorithm With Superresolution Processing: A Prospective Comparative Study. Invest Radiol 2023; 58:842-852. [PMID: 37428618 DOI: 10.1097/rli.0000000000000997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
OBJECTIVES Diffusion-weighted imaging (DWI) enhances specificity in multiparametric breast MRI but is associated with longer acquisition time. Deep learning (DL) reconstruction may significantly shorten acquisition time and improve spatial resolution. In this prospective study, we evaluated acquisition time and image quality of a DL-accelerated DWI sequence with superresolution processing (DWI DL ) in comparison to standard imaging including analysis of lesion conspicuity and contrast of invasive breast cancers (IBCs), benign lesions (BEs), and cysts. MATERIALS AND METHODS This institutional review board-approved prospective monocentric study enrolled participants who underwent 3 T breast MRI between August and December 2022. Standard DWI (DWI STD ; single-shot echo-planar DWI combined with reduced field-of-view excitation; b-values: 50 and 800 s/mm 2 ) was followed by DWI DL with similar acquisition parameters and reduced averages. Quantitative image quality was analyzed for region of interest-based signal-to-noise ratio (SNR) on breast tissue. Apparent diffusion coefficient (ADC), SNR, contrast-to-noise ratio, and contrast (C) values were calculated for biopsy-proven IBCs, BEs, and for cysts. Two radiologists independently assessed image quality, artifacts, and lesion conspicuity in a blinded independent manner. Univariate analysis was performed to test differences and interrater reliability. RESULTS Among 65 participants (54 ± 13 years, 64 women) enrolled in the study, the prevalence of breast cancer was 23%. Average acquisition time was 5:02 minutes for DWI STD and 2:44 minutes for DWI DL ( P < 0.001). Signal-to-noise ratio measured in breast tissue was higher for DWI STD ( P < 0.001). The mean ADC values for IBC were 0.77 × 10 -3 ± 0.13 mm 2 /s in DWI STD and 0.75 × 10 -3 ± 0.12 mm 2 /s in DWI DL without significant difference when sequences were compared ( P = 0.32). Benign lesions presented with mean ADC values of 1.32 × 10 -3 ± 0.48 mm 2 /s in DWI STD and 1.39 × 10 -3 ± 0.54 mm 2 /s in DWI DL ( P = 0.12), and cysts presented with 2.18 × 10 -3 ± 0.49 mm 2 /s in DWI STD and 2.31 × 10 -3 ± 0.43 mm 2 /s in DWI DL . All lesions presented with significantly higher contrast in the DWI DL ( P < 0.001), whereas SNR and contrast-to-noise ratio did not differ significantly between DWI STD and DWI DL regardless of lesion type. Both sequences demonstrated a high subjective image quality (29/65 for DWI STD vs 20/65 for DWI DL ; P < 0.001). The highest lesion conspicuity score was observed more often for DWI DL ( P < 0.001) for all lesion types. Artifacts were scored higher for DWI DL ( P < 0.001). In general, no additional artifacts were noted in DWI DL . Interrater reliability was substantial to excellent (k = 0.68 to 1.0). CONCLUSIONS DWI DL in breast MRI significantly reduced scan time by nearly one half while improving lesion conspicuity and maintaining overall image quality in a prospective clinical cohort.
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Affiliation(s)
- Caroline Wilpert
- From the Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (C.W., C.N., A.R., H.S., M.B., JW, F.B., M.W.-B., J.N.); MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany (T.B., E.W.); EMEA Scientific Partnerships, Siemens Healthcare GmbH, Erlangen, Germany (R.S.); Medical Physics, Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (M.R.); and Department of Stereotactic and Functional Neurosurgery, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (M.R.)
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Chen Q, Fang S, Yuchen Y, Li R, Deng R, Chen Y, Ma D, Lin H, Yan F. Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value. Eur J Radiol 2023; 168:111149. [PMID: 37862927 DOI: 10.1016/j.ejrad.2023.111149] [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: 06/05/2023] [Revised: 09/26/2023] [Accepted: 10/10/2023] [Indexed: 10/22/2023]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect of deep learning reconstruction (DLR) on image quality and apparent diffusion coefficient (ADC) quantification of liver DWI at 3 Tesla. METHOD In this prospective study, images of the liver obtained at DWI with b-values of 0 (DWI0), 50 (DWI50) and 800 s/mm2 (DWI800) from consecutive patients with liver lesions from February 2022 to February 2023 were reconstructed with and without DLR (non-DLR). Image quality was assessed qualitatively using Likert scoring system and quantitatively using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and liver/parenchyma boundary sharpness from region-of-interest (ROI) analysis. ADC value of lesion were measured. Phantom experiment was also performed to investigate the factors that determine the effect of DLR on ADC value. Qualitative score, SNR, CNR, boundary sharpness, and apparent diffusion coefficients (ADCs) for DWI were compared using paired t-test and Wilcoxon signed rank test. P < 0.05 was considered statistically significant. RESULTS A total of 85 patients with 170 lesions were included. DLR group showed a higher qualitative score than the non-DLR group. for example, with DWI800 the score was 4.77 ± 0.52 versus 4.30 ± 0.63 (P < 0.001). DLR group also showed higher SNRs, CNRs and boundary sharpness than the non-DLR group. DLR reduced the ADC of malignant tumors (1.105[0.904, 1.340] versus 1.114[0.904, 1.320]) (P < 0.001), but there was no significant difference in the diagnostic value of malignancy for DLR and non-DLR groups (P = 57.3). The phantom study confirmed a reduction of ADC in images with low resolution, and a stronger reduction of ADC in heterogeneous structures than in homogeneous ones (P < 0.001). CONCLUSIONS DLR improved image quality of liver DWI. DLR reduced the ADC value of lesions, but did not affect the diagnostic performance of ADC in distinguishing malignant tumors on a 3.0-T MRI system.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China; Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin 300060, China
| | - Shu Fang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yang Yuchen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Rong Deng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yongjun Chen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Di Ma
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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11
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Sauer ST, Christner SA, Schlaiß T, Metz C, Schmid A, Kunz AS, Pabst T, Weiland E, Benkert T, Bley TA, Grunz JP. Diffusion-weighted Breast MRI at 3 Tesla: Improved Lesion Visibility and Image Quality with a Combination of Water-excitation and Spectral Fat Saturation. Acad Radiol 2023; 30:1773-1783. [PMID: 36764882 DOI: 10.1016/j.acra.2023.01.014] [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: 11/16/2022] [Revised: 01/05/2023] [Accepted: 01/12/2023] [Indexed: 02/10/2023]
Abstract
RATIONALE AND OBJECTIVES In breast MRI with diffusion-weighted imaging (DWI), fat suppression is essential for eliminating the dominant lipid signal. This investigation evaluates a combined water-excitation-spectral-fatsat method (WEXfs) versus standard spectral attenuated inversion recovery (SPAIR) in high-resolution 3-Tesla breast MRI. MATERIALS AND METHODS Multiparametric breast MRI with 2 echo-planar DWI sequences was performed in 83 patients (50.1 ± 12.6 years) employing either WEXfs or SPAIR for fat signal suppression. Three radiologists assessed overall DWI quality and delineability of 88 focal lesions (28 malignant, 60 benign) on images with b values of 800 and 1600 s/mm2, as well as apparent diffusion coefficient (ADC) maps. For each fat suppression method and b value, the longest lesion diameter was determined in addition to measuring the signal intensity in DWI and ADC value in standardized regions of interest. RESULTS Regardless of b values, image quality (all p < 0.001) and lesion delineability (all p ≤ 0.003) with WEXfs-DWI were deemed superior compared to SPAIR-DWI in benign and malignant lesions. Irrespective of lesion characterization, WEXfs-DWI provided superior signal-to-noise, contrast-to-noise and signal-intensity ratios with 1600 s/mm2 (all p ≤ 0.05). The lesion size difference between contrast-enhanced T1 subtraction images and DWI was smaller for WEXfs compared to SPAIR fat suppression (all p ≤ 0.007). The mean ADC value in malignant lesions was lower for WEXfs-DWI (p < 0.001), while no significant ADC difference was ascertained between both techniques in benign lesions (p = 0.947). CONCLUSION WEXfs-DWI provides better subjective and objective image quality than standard SPAIR-DWI, resulting in a more accurate estimation of benign and malignant lesion size.
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Affiliation(s)
- Stephanie Tina Sauer
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Sara Aniki Christner
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Tanja Schlaiß
- Department of Obstetrics and Gynecology, University Hospital Würzburg, Würzburg, Germany
| | - Corona Metz
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Pediatric Radiology, Berlin, Germany
| | - Andrea Schmid
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Pediatric Radiology, Berlin, Germany
| | - Andreas Steven Kunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Thomas Pabst
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Elisabeth Weiland
- MRI Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- MRI Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thorsten Alexander Bley
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Jan-Peter Grunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany.
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12
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Ursprung S, Herrmann J, Joos N, Weiland E, Benkert T, Almansour H, Lingg A, Afat S, Gassenmaier S. Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging. Eur J Radiol 2023; 165:110953. [PMID: 37399667 DOI: 10.1016/j.ejrad.2023.110953] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity in the diagnosis of the most common solid cancer in men. However, the capacity of MRI systems is limited. Here we investigate the ability of deep learning image reconstruction to accelerate time consuming diffusion-weighted imaging (DWI) acquisition while maintaining diagnostic image quality. METHOD In this retrospective study, raw data of DWI sequences of consecutive patients undergoing MRI of the prostate at a tertiary care hospital in Germany were reconstructed using standard and deep learning reconstruction. To simulate a shortening of acquisition times by 39 %, one instead of two and six instead of ten averages were used in the reconstruction of b = 0 and 1000 s/mm2 images, respectively. Image quality was assessed by three radiologists and objective image quality metrics. RESULTS After the application of exclusion criteria, 35 out of 147 patients examined between September 2022 and January 2023 were included in this study. The radiologists perceived less image noise on deep learning reconstructed images at b = 0 s/mm2 images and ADC maps with good inter-reader agreement. Signal-to-noise ratios were similar overall with discretely reduced values in the transitional zone after deep learning reconstruction. CONCLUSIONS An acquisition time reduction of 39 % without loss in image quality is feasible in DWI of the prostate when using deep learning image reconstruction.
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Affiliation(s)
- Stephan Ursprung
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Judith Herrmann
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Natalie Joos
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Haidara Almansour
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Andreas Lingg
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Saif Afat
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany.
| | - Sebastian Gassenmaier
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
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Afat S, Herrmann J, Almansour H, Benkert T, Weiland E, Hölldobler T, Nikolaou K, Gassenmaier S. Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction. Diagn Interv Imaging 2023; 104:178-184. [PMID: 36787419 DOI: 10.1016/j.diii.2022.11.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/01/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE The purpose of this study was to investigate the impact of deep learning accelerated diffusion-weighted imaging (DWIDL) in 1.5-T liver MRI on image quality, sharpness, and diagnostic confidence. MATERIALS AND METHODS One-hundred patients who underwent liver MRI at 1.5-T including DWI with two different b-values (50 and 800 s/mm²) between February and April 2022 were retrospectively included. There were 54 men and 46 women, with a mean age of 59 ± 14 (SD) years (range: 21-88 years). The single average raw data were retrospectively processed using a deep learning (DL) image reconstruction algorithm leading to a simulated acquisition time of 1 min 28 s for DWIDL as compared to 2 min 31 s for standard DWI (DWIStd) via reduction of signal averages. All DWI datasets were reviewed by four radiologists using a Likert scale ranging from 1-4 using the following criteria: noise level, extent of artifacts, sharpness, overall image quality, and diagnostic confidence. Furthermore, quantitative assessment of noise and signal-to-noise ratio (SNR) was performed via regions of interest. RESULTS No significant differences were found regarding artifacts and overall image quality (P > 0.05). Noise measurements for the spleen, liver, and erector spinae muscles revealed significantly lower noise for DWIDL versus DWIStd (P < 0.001). SNR measurements in the above-mentioned tissues also showed significantly superior results for DWIDL versus DWIStd for b = 50 s/mm² and ADC maps (all P < 0.001). For b = 800 s/mm², significantly superior results were found for the spleen, right hemiliver, and erector spinae muscles. CONCLUSIONS DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality.
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Affiliation(s)
- Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany.
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Thomas Benkert
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, Erlangen 91052, Germany
| | - Elisabeth Weiland
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, Erlangen 91052, Germany
| | - Thomas Hölldobler
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany; Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
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Son JS, Park HS, Park S, Kim YJ, Yu MH, Jung SI, Paek M, Nickel MD. Motion-Corrected versus Conventional Diffusion-Weighted Magnetic Resonance Imaging of the Liver Using Non-Rigid Registration. Diagnostics (Basel) 2023; 13:diagnostics13061008. [PMID: 36980314 PMCID: PMC10047344 DOI: 10.3390/diagnostics13061008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/09/2023] Open
Abstract
It is challenging to overcome motion artifacts in diffusion-weighted imaging (DWI) of the abdomen. This study aimed to evaluate the image quality of motion-corrected DWI of the liver using non-rigid registration in comparison with conventional DWI (c-DWI) in patients with liver diseases. Eighty-nine patients who underwent 3-T magnetic resonance imaging (MRI) of the liver were retrospectively included. DWI was performed using c-DWI and non-rigid motion-corrected (moco) DWI was performed in addition to c-DWI. The image quality and conspicuity of hepatic focal lesions were scored using a five-point scale by two radiologists and compared between the two DWI image sets. The apparent diffusion coefficient (ADC) was measured in three regions of the liver parenchyma and in hepatic focal lesions, and compared between the two DWI image sets. Moco-DWI achieved higher scores in image quality compared to c-DWI in terms of liver edge sharpness and hepatic vessel margin delineation. The conspicuity scores of hepatic focal lesions were higher in moco-DWI. The standard deviation values of ADC of the liver parenchyma were lower in the moco-DWI than in the c-DWI. Moco-DWI using non-rigid registration showed improved overall image quality and provided more reliable ADC measurement, with an equivalent scan time, compared with c-DWI.
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Affiliation(s)
- Je Seung Son
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
| | - Hee Sun Park
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
- Department of Radiology, Konkuk University School of Medicine, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
- Correspondence: ; Tel.: +82-2-2030-5497; Fax: +82-2-2030-7748
| | - Sungeun Park
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
| | - Young Jun Kim
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
- Department of Radiology, Konkuk University School of Medicine, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
| | - Mi Hye Yu
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
- Department of Radiology, Konkuk University School of Medicine, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
| | - Sung Il Jung
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
- Department of Radiology, Konkuk University School of Medicine, 120-1, Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea
| | - Munyoung Paek
- Department of Diagnostic Imaging, Siemens Healthineers Ltd., The Asset Bldg. 10F, 14 Seocho-Daero 74-gil, Seocho-gu, Seoul 06620, Republic of Korea
| | - Marcel Dominik Nickel
- MR Application Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany
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Ringe KI, Yoon JH. Strategies and Techniques for Liver Magnetic Resonance Imaging: New and Pending Applications for Routine Clinical Practice. Korean J Radiol 2023; 24:180-189. [PMID: 36788770 PMCID: PMC9971842 DOI: 10.3348/kjr.2022.0838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/11/2022] [Accepted: 12/22/2022] [Indexed: 02/16/2023] Open
Affiliation(s)
- Kristina I. Ringe
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
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Ji Lee E, Chang YW, Kon Sung J, Thomas B. Feasibility of deep learning k-space-to-image reconstruction for diffusion weighted imaging in patients with breast cancers: focus on image quality and reduced scan time. Eur J Radiol 2022; 157:110608. [DOI: 10.1016/j.ejrad.2022.110608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/29/2022] [Accepted: 11/08/2022] [Indexed: 11/14/2022]
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