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Rajamohan N, Bagga B, Bansal B, Ginocchio L, Gupta A, Chandarana H. Deep Learning-accelerated MRI in Body and Chest. J Comput Assist Tomogr 2025:00004728-990000000-00459. [PMID: 40360272 DOI: 10.1097/rct.0000000000001762] [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: 12/23/2024] [Accepted: 03/27/2025] [Indexed: 05/15/2025]
Abstract
Deep learning reconstruction (DLR) provides an elegant solution for MR acceleration while preserving image quality. This advancement is crucial for body imaging, which is frequently marred by the increased likelihood of motion-related artifacts. Multiple vendor-specific models focusing on T2, T1, and diffusion-weighted imaging have been developed for the abdomen, pelvis, and chest, with the liver and prostate being the most well-studied organ systems. Variational networks with supervised DL models, including data consistency layers and regularizers, are the most common DLR methods. The common theme for all single-center studies on this subject has been noninferior or superior image quality metrics and lesion conspicuity to conventional sequences despite significant acquisition time reduction. DLR also provides a potential for denoising, artifact reduction, increased resolution, and increased signal-noise ratio (SNR) and contrast-to-noise ratio (CNR) that can be balanced with acceleration benefits depending on the imaged organ system. Some specific challenges faced by DLR include slightly reduced lesion detection, cardiac motion-related signal loss, regional SNR variations, and variabilities in ADC measurements as reported in different organ systems. Continued investigations with large-scale multicenter prospective clinical validation of DLR to document generalizability and demonstrate noninferior diagnostic accuracy with histopathologic correlation are the need of the hour. The creation of vendor-neutral solutions, open data sharing, and diversifying training data sets are also critical to strengthening model robustness.
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Affiliation(s)
- Naveen Rajamohan
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX
| | - Barun Bagga
- Department of Radiology, NYU Grossman Long Island School of Medicine, Mineola, NY
| | - Bhavik Bansal
- All India Institute of Medical Sciences, New Delhi, India
| | - Luke Ginocchio
- Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Amit Gupta
- All India Institute of Medical Sciences, New Delhi, India
| | - Hersh Chandarana
- Department of Radiology, Center for Advanced Imaging Innovation and Research, NYU Grossman School of Medicine, New York, NY
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Brendel JM, Dehdab R, Herrmann J, Ursprung S, Werner S, Almansour H, Weiland E, Nickel D, Nikolaou K, Afat S, Gassenmaier S. Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography. LA RADIOLOGIA MEDICA 2025; 130:714-722. [PMID: 40100541 DOI: 10.1007/s11547-025-01987-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2024] [Accepted: 02/25/2025] [Indexed: 03/20/2025]
Abstract
PURPOSE This study aimed to compare a conventional three-dimensional (3-D) magnetic resonance cholangiopancreatography (MRCP) sequence with a deep learning (DL)-accelerated MRCP sequence (hereafter, MRCPDL) regarding acquisition time and image quality. MATERIALS AND METHODS We conducted a prospective study of consecutive patients referred for MRCP between November 2023 and April 2024 at a single tertiary center. Each participant underwent 1.5T 3-D T2-weighted turbo spin echo MRCP using both a conventional sequence (threefold acceleration) and MRCPDL (eightfold acceleration). Three blinded readers independently evaluated image quality, including background signal suppression, bile and pancreatic duct visibility, artifact level, and diagnostic confidence on an ordinal four-point scale. Acquisition times were compared using a paired t-test. Image quality parameters were assessed with repeated measures ANOVA. Interreader agreement was analyzed using Fleiss' κ. RESULTS Out of 419 consecutive patients, 30 participants were evaluated (mean age, 63 ± 15 years; 16 men, 14 women). The mean acquisition time was 10:30 ± 03:04 min for conventional MRCP and 3:57 ± 01:13 min for MRCPDL, P < 0.001. MRCPDL reduced acquisition time by 62.4%. Artifact levels were rated at 3.17 ± 0.77 for conventional MRCP and 3.56 ± 0.66 for MRCPDL (P = 0.041). Background signal suppression, bile duct visibility, pancreatic duct visibility, and diagnostic confidence did not differ significantly (P > 0.05). Interreader agreement was substantial to almost perfect (κ: 0.64-87). CONCLUSIONS Deep learning-accelerated 3-D MRCP reduced acquisition time by 62%, minimized artifacts, and preserved bile and pancreatic duct visibility, supporting its adoption in routine clinical practice.
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Affiliation(s)
- Jan M Brendel
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
| | - Reza Dehdab
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
| | - Judith Herrmann
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
| | - Stephan Ursprung
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
| | - Sebastian Werner
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
| | - Haidara Almansour
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthineers, Forchheim, Germany
| | - Dominik Nickel
- MR Application Predevelopment, Siemens Healthineers, Forchheim, Germany
| | - Konstantin Nikolaou
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076, Tuebingen, Germany
| | - Saif Afat
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Radiology, Diagnostic and Interventional Radiology, Tuebingen University Hospital, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany.
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Zhang M, Xia C, Tang J, Yao L, Hu N, Li J, Peng W, Hu S, Ye Z, Zhang X, Huang J, Li Z. Evaluation of high-resolution pituitary dynamic contrast-enhanced MRI using deep learning-based compressed sensing and super-resolution reconstruction. Eur Radiol 2025:10.1007/s00330-025-11574-5. [PMID: 40221940 DOI: 10.1007/s00330-025-11574-5] [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: 06/22/2024] [Revised: 02/04/2025] [Accepted: 03/06/2025] [Indexed: 04/15/2025]
Abstract
OBJECTIVE This study aims to assess diagnostic performance of high-resolution dynamic contrast-enhanced (DCE) MRI with deep learning-based compressed sensing and super-resolution (DLCS-SR) reconstruction for identifying microadenomas. MATERIALS AND METHODS This prospective study included 126 participants with suspected pituitary microadenomas who underwent DCE MRI between June 2023 and January 2024. Four image groups were derived from single-scan DCE MRI, which included 1.5-mm slice thickness images using DLCS-SR (1.5-mm DLCS-SR images), 1.5-mm slice thickness images with deep learning-based compressed sensing reconstruction (1.5-mm DLCS images), 1.5-mm routine images, and 3-mm slice thickness images using DLCS-SR (3-mm DLCS-SR images). Diagnostic criteria were established by incorporating laboratory findings, clinical symptoms, medical histories, previous imaging, and certain pathologic reports. Two readers assessed the diagnostic performance in identifying pituitary abnormalities and microadenomas. Diagnostic agreements were assessed using κ statistics, and intergroup comparisons for microadenoma detection were performed using the DeLong and McNemar tests. RESULTS The 1.5-mm DLCS-SR images (κ = 0.746-0.848) exhibited superior diagnostic agreement, outperforming 1.5-mm DLCS (κ = 0.585-0.687), 1.5-mm routine (κ = 0.449-0.487), and 3-mm DLCS-SR images (κ = 0.347-0.369) (p < 0.001 for all). Additionally, the performance of 1.5-mm DLCS-SR images in identifying microadenomas [area under the receiver operating characteristic curve (AUC), 0.89-0.94] surpassed that of 1.5-mm DLCS (AUC, 0.83-0.87; p = 0.042 and 0.011, respectively), 1.5-mm routine (AUC, 0.76-0.78; p < 0.001), and 3-mm DLCS-SR images (AUC, 0.72-0.74; p < 0.001). CONCLUSION The findings revealed superior diagnostic performance of 1.5-mm DLCS-SR images in identifying pituitary abnormalities and microadenomas, indicating the clinical-potential of high-resolution DCE MRI. KEY POINTS Question What strategies can overcome the resolution limitations of conventional dynamic contrast-enhanced (DCE) MRI, and which contribute to a high false-negative rate in diagnosing pituitary microadenomas? Findings Deep learning-based compressed sensing and super-resolution reconstruction applied to DCE MRI achieved high resolution while improving image quality and diagnostic efficacy. Clinical relevance DCE MRI with a 1.5-mm slice thickness and high in-plane resolution, utilizing deep learning-based compressed sensing and super-resolution reconstruction, significantly enhances diagnostic accuracy for pituitary abnormalities and microadenomas, enabling timely and effective patient management.
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Affiliation(s)
- Meng Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Tang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Li Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Na Hu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jiaqi Li
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Wanlin Peng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Sixian Hu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | | | - Jin Huang
- Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, China.
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
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Kang Y, Kim SY, Kim JH, Son NH, Park CJ. Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment. BMC Med Imaging 2025; 25:102. [PMID: 40148785 PMCID: PMC11951731 DOI: 10.1186/s12880-025-01647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2024] [Accepted: 03/19/2025] [Indexed: 03/29/2025] Open
Abstract
BACKGROUND To evaluate the performance of a deep learning reconstruction (DLR) based on Adaptive-Compressed sensing (CS)-Network for brain MRI and validate it in a clinical setting. METHODS Ten healthy volunteers and 22 consecutive patients were prospectively enrolled. Volunteers underwent 3D brain MRI including T1 without CS factor (9:16 min, reference standard); with CS factor of 2 without DLR (CS2, 4:6 min); with CS factor of 2 with DLR (DLR-CS2); with CS factor of 4 without DLR (CS4, 2:6 min); and with CS factor of 4 with DLR (DLR-CS4). The patients' MRI included the CS2 and DLR-CS4. The volumes of lateral ventricles, hippocampus, choroid plexus, and white matter hypointensity were calculated and compared among the sequences. Three radiologists independently assessed anatomical conspicuity, overall image quality, artifacts, signal-to-noise ratio (SNR), and sharpness using a 5-point scale for each sequence. RESULTS Applying acceleration factors of 2 and 4 reduced the scan time to 65.4% and 33.5%, respectively, of that of the reference standard. Volumes of all the measured subregions showed no significant differences among different sequences in all participants. In qualitative analysis, the interrater agreement was excellent (κ = 0.844-0.926). In volunteers, quality of DLR-CS4 were comparable to those of CS2 for all metrics except for the overall image quality and SNR despite a 51.2% scan time reduction. In patients, DLR-CS4 showed quality comparable to that of CS2 for all metrics. CONCLUSIONS DLR allowed the scan time reduction by at least half without sacrificing image quality and volumetric quantification accuracy, supporting its reliability and efficiency.
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Affiliation(s)
- Yeseul Kang
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, 363 Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea
| | - Sang-Young Kim
- MR Clinical Science, Philips Healthcare, Seoul, Republic of Korea
| | - Jun Hwee Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, 363 Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea
| | - Nak-Hoon Son
- Department of Statistics, Keimyung University, Daegu, Republic of Korea
| | - Chae Jung Park
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, 363 Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea.
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Kondo S, Nakamura Y, Higaki T, Nishihara T, Takizawa M, Shirai T, Fujimori M, Bito Y, Narita K, Fonseca D, Maeda S, Kawashita I, Honda Y, Awai K. Utility of under-sampled scans with iterative reconstruction and high-frequency preserving transform for high spatial resolution magnetic resonance cholangiopancreatography. Jpn J Radiol 2025; 43:463-471. [PMID: 39496864 PMCID: PMC11868363 DOI: 10.1007/s11604-024-01688-z] [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: 09/06/2024] [Accepted: 10/22/2024] [Indexed: 11/06/2024]
Abstract
PURPOSE Under-sampled scans with iterative reconstruction and high-frequency preserving transform (Us-IRHF) can increase the acquisition speed without degrading the image quality by recovering image information from under-sampled data. We investigate the clinical applicability of high spatial resolution magnetic resonance cholangiopancreatography (MRCP) images without extending the scanning time using Us-IRHF. METHODS A slit phantom was scanned with conventional- (without Us-IRHF), Us-IR- (without HF), and Us-IRHF scanning. The matrix size was 320 × 320 for Us-IR- and Us-IRHF- and 288 × 208 for conventional scanning. Modulation transfer function (MTF) focused on the 1.0 lp/cm gauge for each scanning was calculated. For clinical study we acquired respiratory-triggered 3D MRCP scans with and without Us-IRHF (U+-, U-MRCP) in 41 patients. The matrix size was 320 × 320 for U+- and 288 × 208 for U-MRCP. The acquisition time and the relative duct-to-periductal contrast ratios (RCs) for the right- and left intrahepatic bile-, the common bile-, and the main pancreatic duct were recorded. Visualization of each duct and overall image quality was scored on 5-point confidence scales. For visualization of each duct the score ranged from 1 (not visible) to 5 (visible with excellent details), for the image quality, it ranged from 1 (undiagnostic) to 5 (excellent). Superiority for the qualitative visualization score and non-inferiority for the RC values with prespecified margins were assessed. RESULTS Phantom study showed that compared to the conventional- and Us-IR (without HF) images, the MTF for the Us-IRHF image revealed the highest response. For clinical study, the mean acquisition time was 161 s for U+- and 165 s for U-MRCP. For all ducts, the RC value of U+MRCP was non-inferior to U-MRCP and the qualitative visualization score assigned to U+MRCP was superior to U-MRCP. CONCLUSION Us-IRHF improved the image quality of high spatial resolution MRCP without extending the scanning time.
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Affiliation(s)
- Shota Kondo
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Yuko Nakamura
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan.
| | - Toru Higaki
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan
- Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City, Hiroshima, 739-8527, Japan
| | - Takashi Nishihara
- FUJIFILM Corporation, 2-1, Shintoyofuta, Kashiwa City, Chiba, 277-0804, Japan
| | - Masahiro Takizawa
- FUJIFILM Corporation, 2-1, Shintoyofuta, Kashiwa City, Chiba, 277-0804, Japan
| | - Toru Shirai
- FUJIFILM Corporation, 2-1, Shintoyofuta, Kashiwa City, Chiba, 277-0804, Japan
| | - Motoshi Fujimori
- FUJIFILM Corporation, 2-1, Shintoyofuta, Kashiwa City, Chiba, 277-0804, Japan
| | - Yoshitaka Bito
- FUJIFILM Corporation, 2-1, Shintoyofuta, Kashiwa City, Chiba, 277-0804, Japan
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Kita 15 jo, Nishi 7 chome, Kita ku, Sapporo City 060-8638, Japan
| | - Keigo Narita
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Dara Fonseca
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Shogo Maeda
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Ikuo Kawashita
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Yukiko Honda
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Kazuo Awai
- Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima City, Hiroshima, 734-8551, Japan
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Sartoretti E, Sartoretti T, Bertulli L, Golshani S, Alfieri A, Hoh T, Maurer A, Mannil M, Binkert CA, Sartoretti-Schefer S. Deep learning constrained compressed sensing reconstruction improves high-resolution three-dimensional (3D) T2-weighted turbo spin echo magnetic resonance imaging (MRI) of the lumbar spine. Clin Radiol 2024; 79:e1514-e1521. [PMID: 39379271 DOI: 10.1016/j.crad.2024.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 08/10/2024] [Accepted: 09/08/2024] [Indexed: 10/10/2024]
Abstract
AIM We sought to assess the image quality of three-dimensional (3D) T2-weighted (T2w) turbo spin echo (TSE) sequences with deep learning (DL)-constrained compressed sensing (CS) reconstruction relative to a reference two-dimensional (2D) T2w TSE sequence for routine clinical lumbar spine MRI. MATERIALS AND METHODS Fifty-three patients underwent imaging of the lumbar spine with a sagittal 2D T2w TSE sequence and with two CS-accelerated 3D T2w TSE sequences (voxel size of 0.4 × 0.4 × 0.5 mm) with CS factors of 7 and 11. The CS-accelerated sequences were reconstructed with iterative reconstruction with wavelet transformation (conventional CS) and secondly with a DL-constrained CS reconstruction (named CS-AI). Two readers graded image quality, based on 8 metrics (overall image quality, presence of image noise, presence of motion artifacts, delineation/conspicuity and clarity of anatomical structures such as the spinal cord, cauda equine nerve roots, cerebrospinal fluid (CSF), intervertebral disc, and bone marrow and intervertebral foramen) using Likert scales. RESULTS Overall inter-readout agreement was substantial (Krippendorff's α = 0.724, 95% confidence interval [CI]: 0.692-0.755). The CS7-AI and CS11-AI sequences were comparable or better than the 2D sequence in all 8 metrics (p < 0.001-p > 0.99). The CS7 and CS11 sequences were comparable or better than the 2D sequence in only 5 and 3 of the 8 metrics, respectively (p < 0.001-p > 0.99). CONCLUSION A DL-constrained CS reconstruction significantly improves the quality of accelerated high-resolution 3D T2w TSE imaging of the lumbar spine. Thus, high-quality imaging in a submillimeter resolution in all three imaging planes can be achieved without compromising the image quality as compared with standard 2D T2w TSE imaging.
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Affiliation(s)
- E Sartoretti
- Institute of Radiology, Kantonsspital Winterthur, Brauerstrasse 15, 8401 Winterthur, Switzerland; University of Zürich, Zürich, Switzerland.
| | - T Sartoretti
- Institute of Radiology, Kantonsspital Winterthur, Brauerstrasse 15, 8401 Winterthur, Switzerland; University of Zürich, Zürich, Switzerland.
| | - L Bertulli
- Clinic of Neurosurgery, Kantonsspital Winterthur, Winterthur, Switzerland; Clinic of Neurosurgery, Kantonsspital St. Gallen, Rorschacherstrasse 95, 9007 St. Gallen, Switzerland.
| | - S Golshani
- Clinic of Neurosurgery, Kantonsspital Winterthur, Winterthur, Switzerland; Clinic Neurosurgery, Kantonsspital Luzern, Spitalstrasse, 6000 Luzern 16, Switzerland.
| | - A Alfieri
- Clinic of Neurosurgery, Kantonsspital Winterthur, Winterthur, Switzerland.
| | - T Hoh
- Philips Healthsystems, Zürich, Switzerland.
| | - A Maurer
- University of Zürich, Zürich, Switzerland; Department of Nuclear Medicine, University Hospital Zürich, Zürich, Switzerland.
| | - M Mannil
- Institute of Diagnostic and Interventional Radiology, Caritas Krankenhaus Bad Mergentheim, 97980 Bad Mergentheim, Germany.
| | - C A Binkert
- Institute of Radiology, Kantonsspital Winterthur, Brauerstrasse 15, 8401 Winterthur, Switzerland; University of Zürich, Zürich, Switzerland.
| | - S Sartoretti-Schefer
- Institute of Radiology, Kantonsspital Winterthur, Brauerstrasse 15, 8401 Winterthur, Switzerland; University of Zürich, Zürich, Switzerland.
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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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Duan T, Zhang Z, Chen Y, Bashir MR, Lerner E, Qu Y, Chen J, Zhang X, Song B, Jiang H. Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study. Magn Reson Imaging 2024; 111:74-83. [PMID: 38604347 DOI: 10.1016/j.mri.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/05/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
PURPOSE To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC). METHODS This single-center prospective study enrolled consecutive at-risk participants who underwent 3.0 T gadoxetate disodium-enhanced MRI. Conventional DWI was acquired using parallel imaging (PI) with SENSE (PI-DWI). In CS-DWI and DL-CS-DWI, CS but not PI with SENSE was used to accelerate the scan with 2.5 as the acceleration factor. Qualitative and quantitative image quality were independently assessed by two masked reviewers, and were compared using the Wilcoxon signed-rank test. The detection rates of clinically-relevant (LR-4/5/M based on the Liver Imaging Reporting and Data System v2018) liver lesions for each DWI sequence were independently evaluated by another two masked reviewers against their consensus assessments based on all available non-DWI sequences, and were compared by the McNemar test. RESULTS 67 participants (median age, 58.0 years; 56 males) with 197 clinically-relevant liver lesions were enrolled. Among the three DWI sequences, DL-CS-DWI showed the best qualitative and quantitative image qualities (p range, <0.001-0.039). For clinically-relevant liver lesions, the detection rates (91.4%-93.4%) of DL-CS-DWI showed no difference with CS-DWI (87.3%-89.8%, p = 0.230-0.231) but were superior to PI-DWI (82.7%-85.8%, p = 0.015-0.025). For lesions located in the hepatic dome, DL-CS-DWI demonstrated the highest detection rates (94.8%-97.4% vs 76.9%-79.5% vs 64.1%-69.2%, p = 0.002-0.045) among the three DWI sequences. CONCLUSION In patients at high-risk for HCC, DL-CS-DWI improved image quality and detection for clinically-relevant liver lesions, especially for the hepatic dome.
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Affiliation(s)
- Ting Duan
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
| | - Zhen Zhang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yidi Chen
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Mustafa R Bashir
- Department of Radiology, Center for Advanced Magnetic Resonance in Medicine, Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.
| | - Emily Lerner
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
| | - YaLi Qu
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jie Chen
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xiaoyong Zhang
- Clinical Science, Philips Healthcare, Chengdu 610095, China.
| | - Bin Song
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Hanyu Jiang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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9
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Hirano Y, Fujima N, Kameda H, Ishizaka K, Kwon J, Yoneyama M, Kudo K. High Resolution TOF-MRA Using Compressed Sensing-based Deep Learning Image Reconstruction for the Visualization of Lenticulostriate Arteries: A Preliminary Study. Magn Reson Med Sci 2024:mp.2024-0025. [PMID: 39034144 DOI: 10.2463/mrms.mp.2024-0025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024] Open
Abstract
PURPOSE To investigate the visibility of the lenticulostriate arteries (LSAs) in time-of-flight (TOF)-MR angiography (MRA) using compressed sensing (CS)-based deep learning (DL) image reconstruction by comparing its image quality with that obtained by the conventional CS algorithm. METHODS Five healthy volunteers were included. High-resolution TOF-MRA images with the reduction (R)-factor of 1 were acquired as full-sampling data. Images with R-factors of 2, 4, and 6 were then reconstructed using CS-DL and conventional CS (the combination of CS and sensitivity conceding; CS-SENSE) reconstruction, respectively. In the quantitative assessment, the number of visible LSAs (identified by two radiologists), length of each depicted LSA (evaluated by one radiological technologist), and normalized mean squared error (NMSE) value were assessed. In the qualitative assessment, the overall image quality and the visibility of the peripheral LSA were visually evaluated by two radiologists. RESULTS In the quantitative assessment of the DL-CS images, the number of visible LSAs was significantly higher than those obtained with CS-SENSE in the R-factors of 4 and 6 (Reader 1) and in the R-factor of 6 (Reader 2). The length of the depicted LSAs in the DL-CS images was significantly longer in the R-factor 6 compared to the CS-SENSE result. The NMSE value in CS-DL was significantly lower than in CS-SENSE for R-factors of 4 and 6. In the qualitative assessment of DL-CS images, the overall image quality was significantly higher than that obtained with CS-SENSE in the R-factors 4 and 6 (Reader 1) and in the R-factor 4 (Reader 2). The visibility of the peripheral LSA was significantly higher than that shown by CS-SENSE in all R-factors (Reader 1) and in the R-factors 2 and 4 (Reader 2). CONCLUSION CS-DL reconstruction demonstrated preserved image quality for the depiction of LSAs compared to the conventional CS-SENSE when the R-factor is elevated.
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Affiliation(s)
- Yuya Hirano
- Department of Radiological Technology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Hiroyuki Kameda
- Faculty of Dental Medicine, Department of Radiology, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Kinya Ishizaka
- Department of Radiological Technology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | | | | | - Kohsuke Kudo
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
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10
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Shimada R, Sofue K, Ueno Y, Wakayama T, Yamaguchi T, Ueshima E, Kusaka A, Hori M, Murakami T. Utility of Thin-slice Fat-suppressed Single-shot T2-weighted MR Imaging with Deep Learning Image Reconstruction as a Protocol for Evaluating the Pancreas. Magn Reson Med Sci 2024:mp.2024-0017. [PMID: 38910138 DOI: 10.2463/mrms.mp.2024-0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
PURPOSE To compare the utility of thin-slice fat-suppressed single-shot T2-weighted imaging (T2WI) with deep learning image reconstruction (DLIR) and conventional fast spin-echo T2WI with DLIR for evaluating pancreatic protocol. METHODS This retrospective study included 42 patients (mean age, 70.2 years) with pancreatic cancer who underwent gadoxetic acid-enhanced MRI. Three fat-suppressed T2WI, including conventional fast-spin echo with 6 mm thickness (FSE 6 mm), single-shot fast-spin echo with 6 mm and 3 mm thickness (SSFSE 6 mm and SSFSE 3 mm), were acquired for each patient. For quantitative analysis, the SNRs of the upper abdominal organs were calculated between images with and without DLIR. The pancreas-to-lesion contrast on DLIR images was also calculated. For qualitative analysis, two abdominal radiologists independently scored the image quality on a 5-point scale in the FSE 6 mm, SSFSE 6 mm, and SSFSE 3 mm with DLIR. RESULTS The SNRs significantly improved among the three T2-weighted images with DLIR compared to those without DLIR in all patients (P < 0.001). The pancreas-to-lesion contrast of SSFSE 3 mm was higher than those of the FSE 6 mm (P < 0.001) and tended to be higher than SSFSE 6 mm (P = 0.07). SSFSE 3 mm had the highest image qualities regarding pancreas edge sharpness, pancreatic duct clarity, and overall image quality, followed by SSFSE 6 mm and FSE 6 mm (P < 0.0001). CONCLUSION SSFSE 3 mm with DLIR demonstrated significant improvements in SNRs of the pancreas, pancreas-to-lesion contrast, and image quality more efficiently than did SSFSE 6 mm and FSE 6 mm. Thin-slice fat-suppressed single-shot T2WI with DLIR can be easily implemented for pancreatic MR protocol.
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Affiliation(s)
- Ryuji Shimada
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Hyogo, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Yoshiko Ueno
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Tetsuya Wakayama
- MR Collaborations and Development, GE Healthcare, Hino, Tokyo, Japan
| | - Takeru Yamaguchi
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Eisuke Ueshima
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Akiko Kusaka
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Hyogo, Japan
| | - Masatoshi Hori
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
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11
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Jin D, Li X, Qian Y, Qiao Y, Liu L, Tian J, Wang L, Ma Y, Qin Y, Zhu Y. Modified respiratory-triggered SPACE sequences for magnetic resonance cholangiopancreatography. Eur J Radiol Open 2024; 12:100564. [PMID: 38681662 PMCID: PMC11046076 DOI: 10.1016/j.ejro.2024.100564] [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: 01/19/2024] [Revised: 04/01/2024] [Accepted: 04/15/2024] [Indexed: 05/01/2024] Open
Abstract
Background Respiratory-triggered (RT) and breath-hold are the most common acquisition modalities for magnetic resonance cholangiopancreatography (MRCP). The present study compared the three different acquisition modalities for optimizing the use of MRCP in patients with diseases of the pancreatic and biliary systems. Materials and methods Three MRCP acquisition modalities were used in this study: conventional respiratory-triggered sampling perfection with application-optimized contrasts using different flip evolutions (RT-SPACE), modified RT-SPACE, and breath-hold (BH)-SPACE. Fifty-eight patients with clinically suspected pancreatic and biliary system disease were included. All image data were acquired on a 1.5 T MR. Scan time and image quality were compared between the three acquisition modalities. Friedman test, which was followed by post-hoc analysis, was performed among triple-scan protocol. Results There was a significant difference in the mean acquisition time among conventional RT-SPACE, modified RT-SPACE, and BH-SPACE (167.41±32.11 seconds vs 50.84±73.78 seconds vs 18.00 seconds, P <0.001). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were also significantly different among the three groups (P <0.001). The SNR and CNR were higher in the RT-SPACE group than in the BH-SPACE group (P <0.05). However, there were no statistically significant differences (P >0.05) among the 3 groups regarding quality of overall image, image clarity, background inhibition, and visualization of the pancreatic and biliary system. Conclusions MRCP acquisition with the modified RT-SPACE sequence greatly shortens the acquisition time with comparable quality images. The MRCP acquisition modality could be designed based on the patient's situation to improve the examination pass rate and obtain excellent images for diagnosis.
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Affiliation(s)
| | | | - Yifan Qian
- Department of Radiology, Xi’an Daxing Hospital, Xi’an, China
| | - Yanqiang Qiao
- Department of Radiology, Xi’an Daxing Hospital, Xi’an, China
| | - Liyao Liu
- Department of Radiology, Xi’an Daxing Hospital, Xi’an, China
| | - Juan Tian
- Department of Radiology, Xi’an Daxing Hospital, Xi’an, China
| | - Lei Wang
- Department of Radiology, Xi’an Daxing Hospital, Xi’an, China
| | - Yongli Ma
- Department of Radiology, Xi’an Daxing Hospital, Xi’an, China
| | - Yue Qin
- Department of Radiology, Xi’an Daxing Hospital, Xi’an, China
| | - Yinhu Zhu
- Department of Radiology, Xi’an Daxing Hospital, Xi’an, China
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12
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Tachikawa Y, Maki Y, Ikeda K, Yoshikai H, Toyonari N, Hamano H, Chiwata N, Suzuyama K, Takahashi Y. Flow independent black blood imaging with a large FOV from the neck to the aortic arch: A feasibility study at 3 tesla. Magn Reson Imaging 2024; 108:77-85. [PMID: 38331052 DOI: 10.1016/j.mri.2024.02.001] [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: 09/08/2023] [Revised: 02/03/2024] [Accepted: 02/03/2024] [Indexed: 02/10/2024]
Abstract
PURPOSE To investigate the feasibility of obtaining black-blood imaging with a large FOV from the neck to the aortic arch at 3 T using a newly modified Relaxation-Enhanced Angiography without Contrast and Triggering for Black-Blood Imaging (REACT-BB). MATERIALS AND METHODS REACT-BB provides black-blood images by adjusting the inversion time (TI) in REACT to the null point of blood. The optimal TI for REACT-BB was investigated in 10 healthy volunteers with TI varied from 200 ms to 1400 ms. Contrast ratios were calculated between muscle and three branch arteries of the aortic arch. Additionally, a comparison between REACT-BB and MPRAGE involved evaluating the depiction of high-intensity plaques in 222 patients with stroke or transient ischemic attack. Measurements included plaque-to-muscle signal intensity ratios (PMR), plaque volumes, and carotid artery stenosis rates in 60 patients with high-intensity plaques in carotid arteries. RESULTS REACT-BB with TI = 850 ms produced the black-blood image with the best contrast between blood and background tissues. REACT-BB outperformed MPRAGE in depicting high-intensity plaques in the aortic arch (55.4% vs 45.5%) and exhibited superior overall image quality in visual assessment (3.31 ± 0.70 vs 2.89 ± 0.73; p < 0.05). Although the PMR of REACT-BB was significantly lower than MPRAGE (2.227 ± 0.601 vs 2.285 ± 0.662; P < 0.05), a strong positive correlation existed between REACT-BB and MPRAGE (ρ = 0.935; P < 0.05), and all high-intensity plaques that MPRAGE detected were clearly detected by REACT-BB. CONCLUSION REACT-BB provides black-blood images with uniformly suppressed fat and blood signals over a large FOV from the neck to the aortic arch with comparable or better high-signal plaque depiction than MPRAGE.
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Affiliation(s)
- Yoshihiko Tachikawa
- Division of Radiological Technology, Department of Medical Technology, Karatsu Red Cross Hospital, 2430 Watada, Karatsu, Saga 847-8588, Japan.
| | - Yasunori Maki
- Division of Radiological Technology, Department of Medical Technology, Karatsu Red Cross Hospital, 2430 Watada, Karatsu, Saga 847-8588, Japan
| | - Kento Ikeda
- Division of Radiological Technology, Department of Medical Technology, Karatsu Red Cross Hospital, 2430 Watada, Karatsu, Saga 847-8588, Japan
| | - Hikaru Yoshikai
- Division of Radiological Technology, Department of Medical Technology, Karatsu Red Cross Hospital, 2430 Watada, Karatsu, Saga 847-8588, Japan
| | - Nobuyuki Toyonari
- Department of Radiology, Kumamoto Chuo Hospital, 1-5-1 Tainoshima, Minami-ku, Kumamoto 862-0962, Japan
| | - Hiroshi Hamano
- Philips Japan, Philips Building, 2-13-37 Kohnan, Minato-ku, Tokyo 108-8507, Japan
| | - Naoya Chiwata
- Division of Radiological Technology, Department of Medical Technology, Karatsu Red Cross Hospital, 2430 Watada, Karatsu, Saga 847-8588, Japan
| | - Kenji Suzuyama
- Department of Neurosurgery, Karatsu Red Cross Hospital, 2430 Watada, Karatsu, Saga 847-8588, Japan
| | - Yukihiko Takahashi
- Department of Radiology, Karatsu Red Cross Hospital, 2430 Watada, Karatsu, Saga 847-8588, Japan
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13
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Vollbrecht TM, Hart C, Zhang S, Katemann C, Sprinkart AM, Isaak A, Attenberger U, Pieper CC, Kuetting D, Geipel A, Strizek B, Luetkens JA. Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI. Front Cardiovasc Med 2024; 11:1323443. [PMID: 38410246 PMCID: PMC10894983 DOI: 10.3389/fcvm.2024.1323443] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 02/28/2024] Open
Abstract
Purpose This study aims to evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD). Methods Twenty-five fetuses with CHD (mean gestational age: 35 ± 1 weeks) underwent fetal cardiac MRI at 3T. Cine imaging was acquired using a balanced steady-state free precession (bSSFP) sequence with Doppler ultrasound gating. Images were reconstructed using both compressed sensing (bSSFP CS) and a pre-trained convolutional neural network trained for DL denoising (bSSFP DL). Images were compared qualitatively based on a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent) and quantitatively by calculating the apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR). Diagnostic confidence was assessed for the atria, ventricles, foramen ovale, valves, great vessels, aortic arch, and pulmonary veins. Results Fetal cardiac cine MRI was successful in 23 fetuses (92%), with two studies excluded due to extensive fetal motion. The image quality of bSSFP DL cine reconstructions was rated superior to standard bSSFP CS cine images in terms of contrast [3 (interquartile range: 2-4) vs. 5 (4-5), P < 0.001] and endocardial edge definition [3 (2-4) vs. 4 (4-5), P < 0.001], while the extent of artifacts was found to be comparable [4 (3-4.75) vs. 4 (3-4), P = 0.40]. bSSFP DL images had higher aSNR and aCNR compared with the bSSFP CS images (aSNR: 13.4 ± 6.9 vs. 8.3 ± 3.6, P < 0.001; aCNR: 26.6 ± 15.8 vs. 14.4 ± 6.8, P < 0.001). Diagnostic confidence of the bSSFP DL images was superior for the evaluation of cardiovascular structures (e.g., atria and ventricles: P = 0.003). Conclusion DL image denoising provides superior quality for DUS-gated fetal cardiac cine imaging of CHD compared to standard CS image reconstruction.
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Affiliation(s)
- Thomas M Vollbrecht
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
| | - Christopher Hart
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
- Department of Pediatric Cardiology, University Hospital Bonn, Bonn, Germany
| | - Shuo Zhang
- Philips GmbH Market DACH, PD Clinical Science, Hamburg, Germany
| | | | - Alois M Sprinkart
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
| | - Alexander Isaak
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Claus C Pieper
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Daniel Kuetting
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
| | - Annegret Geipel
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
| | - Brigitte Strizek
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
| | - Julian A Luetkens
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
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