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Mesropyan N, Katemann C, Leutner C, Sommer A, Isaak A, Weber OM, Peeters JM, Dell T, Bischoff L, Kuetting D, Pieper CC, Lakghomi A, Luetkens JA. Accelerated High-resolution T1- and T2-weighted Breast MRI with Deep Learning Super-resolution Reconstruction. Acad Radiol 2025:S1076-6332(24)01043-2. [PMID: 39794159 DOI: 10.1016/j.acra.2024.12.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2024] [Revised: 12/22/2024] [Accepted: 12/24/2024] [Indexed: 01/13/2025]
Abstract
RATIONALE AND OBJECTIVES To assess the performance of an industry-developed deep learning (DL) algorithm to reconstruct low-resolution Cartesian T1-weighted dynamic contrast-enhanced (T1w) and T2-weighted turbo-spin-echo (T2w) sequences and compare them to standard sequences. MATERIALS AND METHODS Female patients with indications for breast MRI were included in this prospective study. The study protocol at 1.5 Tesla MRI included T1w and T2w. Both sequences were acquired in standard resolution (T1S and T2S) and in low-resolution with following DL reconstructions (T1DL and T2DL). For DL reconstruction, two convolutional networks were used: (1) Adaptive-CS-Net for denoising with compressed sensing, and (2) Precise-Image-Net for resolution upscaling of previously downscaled images. Overall image quality was assessed using 5-point-Likert scale (from 1=non-diagnostic to 5=excellent). Apparent signal-to-noise (aSNR) and contrast-to-noise (aCNR) ratios were calculated. Breast Imaging Reporting and Data System (BI-RADS) agreement between different sequence types was assessed. RESULTS A total of 47 patients were included (mean age, 58±11 years). Acquisition time for T1DL and T2DL were reduced by 51% (44 vs. 90 s per dynamic phase) and 46% (102 vs. 192 s), respectively. T1DL and T2DL showed higher overall image quality (e.g., 4 [IQR, 4-4] for T1S vs. 5 [IQR, 5-5] for T1DL, P<0.001). Both, T1DL and T2DL revealed higher aSNR and aCNR than T1S and T2S (e.g., aSNR: 32.35±10.23 for T2S vs. 27.88±6.86 for T2DL, P=0.014). Cohen k agreement by BI-RADS assessment was excellent (0.962, P<0.001). CONCLUSION DL for denoising and resolution upscaling reduces acquisition time and improves image quality for T1w and T2w breast MRI.
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Affiliation(s)
- Narine Mesropyan
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.).
| | | | - Claudia Leutner
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
| | - Alexandra Sommer
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
| | - Alexander Isaak
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
| | | | | | - Tatjana Dell
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
| | - Leon Bischoff
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
| | - Daniel Kuetting
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
| | - Claus C Pieper
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
| | - Asadeh Lakghomi
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
| | - Julian A Luetkens
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany (N.M., C.L., A.S., A.I., T.D., L.B., D.K., C.C.P., A.L., J.A.L.)
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Wilpert C, Schneider H, Rau A, Russe MF, Oerther B, Strecker R, Nickel MD, Weiland E, Haeger A, Benndorf M, Mayrhofer T, Weiss J, Bamberg F, Windfuhr-Blum M, Neubauer J. Faster Acquisition and Improved Image Quality of T2-Weighted Dixon Breast MRI at 3T Using Deep Learning: A Prospective Study. Korean J Radiol 2025; 26:29-42. [PMID: 39780629 PMCID: PMC11717867 DOI: 10.3348/kjr.2023.1303] [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: 12/29/2023] [Revised: 10/15/2024] [Accepted: 10/15/2024] [Indexed: 01/11/2025] Open
Abstract
OBJECTIVE The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI). MATERIALS AND METHODS This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed. RESULTS Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001). CONCLUSION T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.
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Affiliation(s)
- Caroline Wilpert
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Hannah Schneider
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Neuroradiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maximilian Frederic Russe
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Benedict Oerther
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ralph Strecker
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
- EMEA Scientific Partnerships, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Alexa Haeger
- Department of Neurology, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Matthias Benndorf
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Thomas Mayrhofer
- School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
- Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jakob Weiss
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marisa Windfuhr-Blum
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jakob Neubauer
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Zhu L, Shi B, Ding B, Xia Y, Wang K, Feng W, Dai J, Xu T, Wang B, Yuan F, Shen H, Dong H, Zhang H. Accelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01345-x. [PMID: 39663320 DOI: 10.1007/s10278-024-01345-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 11/15/2024] [Accepted: 11/18/2024] [Indexed: 12/13/2024]
Abstract
Deep learning reconstruction (DLR) has exhibited potential in saving scan time. There is limited research on the evaluation of accelerated acquisition with DLR in staging rectal cancers. Our first objective was to explore the best DLR level in saving time through phantom experiments. Resolution and number of excitations (NEX) adjusted for different scan time, image quality of conventionally reconstructed T2W images were measured and compared with images reconstructed with different DLR level. The second objective was to explore the feasibility of accelerated T2W imaging with DLR in image quality and diagnostic performance for rectal cancer patients. 52 patients were prospectively enrolled to undergo accelerated acquisition reconstructed with highly-denoised DLR (DLR_H40sec) and conventional reconstruction (ConR2min). The image quality and diagnostic performance were evaluated by observers with varying experience and compared between protocols using κ statistics and area under the receiver operating characteristic curve (AUC). The phantom experiments demonstrated that DLR_H could achieve superior signal-to-noise ratio (SNR), detail conspicuity, sharpness, and less distortion within the least scan time. The DLR_H40sec images exhibited higher sharpness and SNR than ConR2min. The agreements with pathological TN-stages were improved using DLR_H40sec images compared to ConR2min (T: 0.846vs. 0.771, 0.825vs. 0.700, and 0.697vs. 0.512; N: 0.527vs. 0.521, 0.421vs. 0.348 and 0.517vs. 0.363 for junior, intermediate, and senior observes, respectively). Comparable AUCs to identify T3-4 and N1-2 tumors were achieved using DLR_H40sec and ConR2min images (P > 0.05). Consequently, with 2/3-time reduction, DLR_H40sec images showed improved image quality and comparable TN-staging performance to conventional T2W imaging for rectal cancer patients.
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Affiliation(s)
- Lan Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China
| | - Bowen Shi
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China
| | - Bei Ding
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China
| | - Kangning Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China
| | - Weiming Feng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China
| | - Jiankun Dai
- Department of MR, GE Healthcare, Beijing, China
| | - Tianyong Xu
- Department of MR, GE Healthcare, Beijing, China
| | - Baisong Wang
- Department of Biomedical Statistics, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University of Medicine, No.118 Wansheng Street, Suzhou Industrial Park, Suzhou City, 215028, Jiangsu Province, China.
| | - Haipeng Dong
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China.
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Ruff C, Bombach P, Roder C, Weinbrenner E, Artzner C, Zerweck L, Paulsen F, Hauser TK, Ernemann U, Gohla G. Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction. Eur J Radiol Open 2024; 13:100617. [PMID: 39717474 PMCID: PMC11664152 DOI: 10.1016/j.ejro.2024.100617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2024] [Revised: 11/19/2024] [Accepted: 11/28/2024] [Indexed: 12/25/2024] Open
Abstract
Rationale and Objectives: Diagnostic accuracy and therapeutic decision-making for IDH-mutant gliomas in tumor board reviews are based on MRI and multidisciplinary interactions. Materials and Methods This study explores the feasibility of deep learning-based reconstruction (DLR) in MRI for IDH-mutant gliomas. The research utilizes a multidisciplinary approach, engaging neuroradiologists, neurosurgeons, neuro-oncologists, and radiotherapists to evaluate qualitative aspects of DLR and conventional reconstructed (CR) sequences. Furthermore, quantitative image quality and tumor volumes according to Response Assessment in Neuro-Oncology (RANO) 2.0 standards were assessed. Results All DLR sequences consistently outperformed CR sequences (median of 4 for all) in qualitative image quality across all raters (p < 0.001 for all) and revealed higher SNR and CNR values (p < 0.001 for all). Preference for all DLR over CR was overwhelming, with ratings of 84 % from the neuroradiologist, 100 % from the neurosurgeon, 92 % from the neuro-oncologist, and 84 % from the radiation oncologist. The RANO 2.0 compliant measurements showed no significant difference between the CR and DRL sequences (p = 0.142). Conclusion This study demonstrates the clinical feasibility of DLR in MR imaging of IDH-mutant gliomas, with significant time savings of 29.6 % on average and non-inferior image quality to CR. DLR sequences received strong multidisciplinary preference, underscoring their potential for enhancing neuro-oncological decision-making and suitability for clinical implementation.
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Affiliation(s)
- Christer Ruff
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls-University Tuebingen, Tuebingen D-72076, Germany
| | - Paula Bombach
- Department of Neurology and Interdisciplinary Neuro-Oncology, University Hospital Tuebingen, Tuebingen D-72076, Germany
- Hertie Institute for Clinical Brain Research, Eberhard Karls University Tuebingen Center of Neuro-Oncology, Tuebingen D-72076, Germany
- Center for Neuro-Oncology, Comprehensive Cancer Center Tuebingen-Stuttgart, University Hospital of Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen D-72070, Germany
| | - Constantin Roder
- Center for Neuro-Oncology, Comprehensive Cancer Center Tuebingen-Stuttgart, University Hospital of Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen D-72070, Germany
- Department of Neurosurgery, University of Tuebingen, Tuebingen D-72076, Germany
| | - Eliane Weinbrenner
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls-University Tuebingen, Tuebingen D-72076, Germany
| | - Christoph Artzner
- Department of Diagnostic and Interventional Radiology, Diakonie Klinikum Stuttgart, Stuttgart D-70176, Germany
| | - Leonie Zerweck
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls-University Tuebingen, Tuebingen D-72076, Germany
| | - Frank Paulsen
- Department of Radiation Oncology, University Hospital Tuebingen, Tuebingen D-72076, Germany
| | - Till-Karsten Hauser
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls-University Tuebingen, Tuebingen D-72076, Germany
| | - Ulrike Ernemann
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls-University Tuebingen, Tuebingen D-72076, Germany
| | - Georg Gohla
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls-University Tuebingen, Tuebingen D-72076, Germany
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Gullo RL, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Lipman KG, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024; 60:2290-2308. [PMID: 38581127 PMCID: PMC11452568 DOI: 10.1002/jmri.29358] [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: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Olsson LE, Af Wetterstedt S, Scherman J, Gunnlaugsson A, Persson E, Jamtheim Gustafsson C. Evaluation of a deep learning magnetic resonance imaging reconstruction method for synthetic computed tomography generation in prostate radiotherapy. Phys Imaging Radiat Oncol 2024; 29:100557. [PMID: 38414521 PMCID: PMC10897922 DOI: 10.1016/j.phro.2024.100557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/29/2024] Open
Abstract
Background and Purpose In magnetic resonance imaging (MRI) only radiotherapy computed tomography (CT) is excluded. The method relies entirely on synthetic CT images generated from MRI. This study evaluates the compatibility of a commercial synthetic CT (sCT) with an accelerated commercial deep learning reconstruction (DLR) in MRI-only prostate radiotherapy. Materials and Methods For a group of 24 patients (cohort 1) the effects of DLR were studied in isolation. MRI data were reconstructed conventionally and with DLR from identical k-space data, and sCTs were generated for both reconstructions. The sCT quality, Hounsfield Unit (HU) and dosimetric impact were investigated. In another group of 15 patients (cohort 2) effects on sCT generation using accelerated MRI acquisition (40 % time reduction) reconstructed with DLR were investigated. Results sCT images from both cohorts, generated from DLR MRI data, were of clinically expected image quality. The mean dose differences for targets and organs at risks in cohort 1 were <0.06 Gy, corresponding to a 0.1 % prescribed dose difference. Similar dose differences were observed in cohort 2. Gamma pass rates for cohort 1 were 100 % for criteria 3 %/3mm, 2 %/2mm and 1 %/1mm for all dose levels. Mean error and mean absolute error inside the body, between sCTs, averaged over all cohort 1 subjects, were -1.1 ± 0.6 [-2.4 0.2] and 2.9 ± 0.4 [2.3 3.9] HU, respectively. Conclusions DLR was suitable for sCT generation with clinically negligible differences in HU and calculated dose compared to the conventional MRI reconstruction method. For sCT generation DLR enables scan time reduction, without compromised sCT quality.
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Affiliation(s)
- Lars E Olsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
| | - Sacha Af Wetterstedt
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
| | - Jonas Scherman
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
| | - Adalsteinn Gunnlaugsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
| | - Emilia Persson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
| | - Christian Jamtheim Gustafsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Klinikgatan 5, Lund 221 85, Sweden
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Carl Bertil Laurells gata 9, Malmö 205 02, Sweden
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