{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:03:03Z","timestamp":1787169783267,"version":"build-2736575974"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFF0714200"],"award-info":[{"award-number":["2023YFF0714200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008865","name":"Xiamen University","doi-asserted-by":"publisher","award":["20720220063"],"award-info":[{"award-number":["20720220063"]}],"id":[{"id":"10.13039\/501100008865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["231107173160805"],"award-info":[{"award-number":["231107173160805"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2023J02005"],"award-info":[{"award-number":["2023J02005"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62122064"],"award-info":[{"award-number":["62122064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62331021"],"award-info":[{"award-number":["62331021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.compmedimag.2025.102579","type":"journal-article","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T02:35:52Z","timestamp":1749263752000},"page":"102579","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Physics-informed neural networks for denoising high b-value diffusion-weighted images"],"prefix":"10.1016","volume":"124","author":[{"given":"Qiaoling","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaju","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenze","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoxing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weigen","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobo","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2025.102579_b1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.compmedimag.2018.08.006","article-title":"Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder","volume":"69","author":"Abraham","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2025.102579_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102459","article-title":"Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation","volume":"118","author":"Andrade-Miranda","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102579_b3","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1002\/mrm.25851","article-title":"Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images","volume":"75","author":"Campbell-Washburn","year":"2016","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102579_b4","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0211621","article-title":"Multi-channel framelet denoising of diffusion-weighted images","volume":"14","author":"Chen","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.compmedimag.2025.102579_b5","doi-asserted-by":"crossref","unstructured":"Chen, L., et al., 2022. Simple baselines for image restoration. In: European Conference on Computer Vision. pp. 17\u201333.","DOI":"10.1007\/978-3-031-20071-7_2"},{"key":"10.1016\/j.compmedimag.2025.102579_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107471","article-title":"MSA-YOLOv5: Multi-scale attention-based YOLOv5 for automatic detection of acute ischemic stroke from multi-modality MRI images","volume":"165","author":"Chen","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2025.102579_b7","article-title":"Breast cancer detection using deep learning: Datasets, methods, and challenges ahead","volume":"149","author":"Dar","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102579_b8","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1002\/jmri.20969","article-title":"Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters","volume":"26","author":"Dietrich","year":"2007","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102579_b9","article-title":"Quantitative diffusion MRI in prostate cancer: image quality, what we can measure and how it improves clinical assessment","volume":"161","author":"Fennessy","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.compmedimag.2025.102579_b10","series-title":"CascadedGaze: efficiency in global context extraction for image restoration","author":"Ghasemabadi","year":"2024"},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102579_b11","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1002\/mrm.10171","article-title":"Generalized autocalibrating partially parallel acquisitions (GRAPPA)","volume":"47","author":"Griswold","year":"2002","journal-title":"Magn. Reson. Med."},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102579_b12","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1002\/mrm.1910340618","article-title":"The rician distribution of noisy MRI data","volume":"34","author":"Gudbjartsson","year":"1995","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.compmedimag.2025.102579_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102479","article-title":"Adaptive fusion of dual-view for grading prostate cancer","volume":"119","author":"He","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102579_b14","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1118\/1.595711","article-title":"Measurement of signal intensities in the presence of noise in MR images","volume":"12","author":"Henkelman","year":"1985","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2025.102579_b15","doi-asserted-by":"crossref","unstructured":"Hore, A., Ziou, D., 2010. Image quality metrics: PSNR vs. SSIM. In: Proc. Int. Conf. Pattern Recognit. Istanbul, Turkey. pp. 2366\u20132369.","DOI":"10.1109\/ICPR.2010.579"},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102579_b16","first-page":"3997","article-title":"Protecting prostate cancer classification from rectal artifacts via targeted adversarial training","volume":"28","author":"Hu","year":"2024","journal-title":"IEEE J. Biomed. Heal. Inf."},{"key":"10.1016\/j.compmedimag.2025.102579_b17","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s42058-021-00053-7","article-title":"Effect of b values and size of region of interest on apparent diffusion coefficient measurement and its reproducibility in liver diffusion-weighted MRI","volume":"4","author":"Huang","year":"2021","journal-title":"Chin. J. Acad. Radiol."},{"key":"10.1016\/j.compmedimag.2025.102579_b18","series-title":"Diffusion MRI","author":"Jones","year":"2010"},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102579_b19","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1002\/nbm.1543","article-title":"Twenty-five pitfalls in the analysis of diffusion MRI data","volume":"23","author":"Jones","year":"2010","journal-title":"NMR Biomed."},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102579_b20","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1109\/TBME.2015.2486042","article-title":"A new feature-enhanced speckle reduction method based on multiscale analysis for ultrasound b-mode imaging","volume":"63","author":"Kang","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102579_b21","article-title":"Accelerating prostate diffusion-weighted MRI using a guided denoising convolutional neural network: retrospective feasibility study","volume":"2","author":"Kaye","year":"2020","journal-title":"Radiol.: Artif. Intell."},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102579_b22","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.2214\/AJR.09.3654","article-title":"Diffusion-weighted MRI at 3 T for the evaluation of prostate cancer","volume":"194","author":"Kim","year":"2010","journal-title":"Am. J. Roentgenol."},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102579_b23","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1038\/nrn1119","article-title":"Looking into the functional architecture of the brain with diffusion MRI","volume":"4","author":"Le Bihan","year":"2003","journal-title":"Nature Rev. Neurosci."},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102579_b24","doi-asserted-by":"crossref","first-page":"3334","DOI":"10.1002\/mrm.28937","article-title":"SuperDTI: ultrafast DTI and fiber tractography with deep learning","volume":"86","author":"Li","year":"2021","journal-title":"Magn. Reson. Med."},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102579_b25","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.compmedimag.2012.06.004","article-title":"Improving the correction of eddy current-induced distortion in diffusion-weighted images by excluding signals from the cerebral spinal fluid","volume":"36","author":"Liu","year":"2012","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2025.102579_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102415","article-title":"A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions","volume":"116","author":"Liu","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2025.102579_b27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/imag_a_00060","article-title":"Denoising diffusion MRI: Considerations and implications for analysis","volume":"2","author":"Manzano Patron","year":"2024","journal-title":"Imaging Neurosci."},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102579_b28","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1007\/s00261-018-1804-9","article-title":"Prostate MRI using an external phased array wearable pelvic coil at 3T: comparison with an endorectal coil","volume":"44","author":"O\u2019Donohoe","year":"2019","journal-title":"Abdom. Radiol."},{"issue":"12","key":"10.1016\/j.compmedimag.2025.102579_b29","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.3390\/diagnostics13122111","article-title":"Artificial intelligence for automated DWI\/FLAIR mismatch assessment on magnetic resonance imaging in stroke: a systematic review","volume":"13","author":"Offersen","year":"2023","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102579_b30","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1002\/jmri.28288","article-title":"Computed DWI MRI results in superior capability for N-stage assessment of non-small cell lung cancer than that of actual DWI, STIR imaging, and FDG-PET\/CT","volume":"57","author":"Ohno","year":"2023","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102579_b31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.59275\/j.melba.2022-3g12","article-title":"A review and experimental evaluation of deep learning methods for MRI reconstruction","volume":"1","author":"Pal","year":"2022","journal-title":"Mach. Learn. Biomed. Imaging"},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102579_b32","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TMI.2021.3116298","article-title":"DIFFnet: diffusion parameter mapping network generalized f or input diffusion gradient schemes and b-value","volume":"41","author":"Park","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102579_b33","doi-asserted-by":"crossref","first-page":"653","DOI":"10.3892\/etm.2016.3345","article-title":"Dual-domain denoising in three dimensional magnetic resonance imaging","volume":"12","author":"Peng","year":"2016","journal-title":"Exp. Ther. Med."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102579_b34","doi-asserted-by":"crossref","first-page":"24292","DOI":"10.1038\/s41598-024-75007-x","article-title":"Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation","volume":"14","author":"Pfaff","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compmedimag.2025.102579_b35","series-title":"Physics-informed deep diffusion MRI reconstruction with clinical data evaluation: break training data bottleneck in artificial intelligence","author":"Qian","year":"2022"},{"key":"10.1016\/j.compmedimag.2025.102579_b36","doi-asserted-by":"crossref","unstructured":"Qian, C., et al., 2023. Physics-informed deep diffusion MRI reconstruction: break the bottleneck of training data in artificial intelligence. In: Proc. IEEE Int. Symp. Biomed. Imaging, Cartagena, Colombia. pp. 1\u20135.","DOI":"10.1109\/ISBI53787.2023.10230538"},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102579_b37","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1002\/mrm.28752","article-title":"SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces","volume":"86","author":"Ramos-Llord\u00e9n","year":"2021","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102579_b38","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s00261-014-0181-2","article-title":"Zoomed echo-planar imaging using parallel transmission: impact on image quality of diffusion-weighted imaging of the prostate at 3T","volume":"40","author":"Rosenkrantz","year":"2015","journal-title":"Abdom. Imaging"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102579_b39","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1063\/1.1695690","article-title":"Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient","volume":"42","author":"Stejskal","year":"1965","journal-title":"J. Chem. Phys."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102579_b40","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/s41597-024-03252-w","article-title":"FastMRI prostate: a public, biparametric MRI dataset to advance machine learning for prostate cancer imaging","volume":"11","author":"Tibrewala","year":"2024","journal-title":"Sci. Data"},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102579_b41","first-page":"923","article-title":"Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to MRI liver tumor differentiation","volume":"23","author":"Trivizakis","year":"2018","journal-title":"IEEE J. Biomed. Heal. Inf."},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102579_b42","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eururo.2019.02.033","article-title":"Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2","author":"Turkbey","year":"2019","journal-title":"Eur. Urol."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102579_b43","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1002\/mrm.22014","article-title":"Robust estimation of the apparent diffusion coefficient (ADC) in heterogeneous solid tumors","volume":"62","author":"Walker-Samuel","year":"2009","journal-title":"Magn. Reson. Med."},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102579_b44","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"10.1016\/j.compmedimag.2025.102579_b45","first-page":"4575","article-title":"Multiple b-value model-based residual network (MORN) for accelerated high-resolution diffusion-weighted imaging","volume":"26","author":"Wang","year":"2022","journal-title":"IEEE J. Biomed. Heal. Inf."},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102579_b46","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ad1d6d","article-title":"Ultrafast diffusion tensor imaging based on deep learning and multi-slice information sharing","volume":"69","author":"Wang","year":"2024","journal-title":"Phys. Med. Biol."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102579_b47","article-title":"Denoising diffusion MRI via graph total variance in spatioangular domain","volume":"2021","author":"Wu","year":"2021","journal-title":"Comput. Math. Methods Med."},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102579_b48","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ad45a5","article-title":"FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning","volume":"69","author":"Wu","year":"2024","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.compmedimag.2025.102579_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102414","article-title":"Accurate segmentation of liver tumor from multi-modality non-contrast images using a dual-stream multi-level fusion framework","volume":"116","author":"Xu","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102579_b50","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/MSP.2022.3183809","article-title":"Physics-driven synthetic data learning for biomedical magnetic resonance: the imaging physics-based data synthesis paradigm for artificial intelligence","volume":"40","author":"Yang","year":"2023","journal-title":"IEEE Signal Process. Mag."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102579_b51","first-page":"917","article-title":"Double transformer super-resolution for breast cancer ADC images","volume":"28","author":"Yang","year":"2024","journal-title":"IEEE J. Biomed. Heal. Inf."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102579_b52","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1186\/s12880-021-00727-9","article-title":"A review on deep learning MRI reconstruction without fully sampled k-space","volume":"21","author":"Zeng","year":"2021","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102579_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101926","article-title":"Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time","volume":"90","author":"Zhang","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2025.102579_b54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12880-021-00549-9","article-title":"Non-local means based rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord","volume":"21","author":"Zhang","year":"2021","journal-title":"BMC Med. Imaging"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102579_b55","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1109\/TCC.2024.3476418","article-title":"CloudBrain-reconai: a cloud computing platform for MRI reconstruction and radiologists\u2019 image quality evaluation","volume":"12","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Cloud Comput."},{"key":"10.1016\/j.compmedimag.2025.102579_b56","first-page":"1","article-title":"Deep learning-based reconstruction on intensity-inhomogeneous diffusion magnetic resonance imaging","author":"Zhu","year":"2024","journal-title":"Iradiology"},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102579_b57","article-title":"Accelerating whole-body diffusion-weighted MRI with deep learning-based denoising image filters","volume":"3","author":"Zormpas-Petridis","year":"2021","journal-title":"Radiol.: Artif. Intell."}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125000886?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125000886?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T04:17:21Z","timestamp":1762316241000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611125000886"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":57,"alternative-id":["S0895611125000886"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102579","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Physics-informed neural networks for denoising high b-value diffusion-weighted images","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102579","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102579"}}