{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T09:50:31Z","timestamp":1785750631407,"version":"3.56.0"},"reference-count":41,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.82271956"],"award-info":[{"award-number":["No.82271956"]}],"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":["81727806"],"award-info":[{"award-number":["81727806"]}],"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":["11774231"],"award-info":[{"award-number":["11774231"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Proton resonance frequency (PRF)\u2014based magnetic resonance (MR) thermometry plays a critical role in thermal ablation therapies through focused ultrasound (FUS). For clinical applications, accurate and rapid temperature feedback is essential to ensure both the safety and effectiveness of these treatments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This work aims to improve temporal resolution in dynamic MR temperature map reconstructions using an enhanced deep\u2010learning method, thereby supporting the real\u2010time monitoring required for effective FUS treatments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Five classical neural network architectures\u2010cascade net, complex\u2010valued U\u2010Net, shift window transformer for MRI, real\u2010valued U\u2010Net, and U\u2010Net with residual blocks\u2010along with training\u2010optimized methods were applied to reconstruct temperature maps from 2\u2010fold and 4\u2010fold undersampled k\u2010space data. The training enhancements included pre\u2010training\/training\u2010phase data augmentations, knowledge distillation, and a novel amplitude\u2010phase decoupling loss function. Phantom and ex vivo tissue heating experiments were conducted using a FUS transducer. Ground truth was the complex MR images with accurate temperature changes, and datasets were manually undersampled to simulate such acceleration here. Separate testing datasets were used to evaluate real\u2010time performance and temperature accuracy. Furthermore, our proposed deep learning\u2010based rapid reconstruction approach was validated on a clinical dataset obtained from patients with uterine fibroids, demonstrating its clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Acceleration factors of 1.9 and 3.7 were achieved for 2\u00d7 and 4\u00d7 k\u2010space under samplings, respectively. The deep learning\u2010based reconstruction using ResUNet incorporating the four optimizations, showed superior performance. For 2\u2010fold acceleration, the RMSE of temperature map patches were 0.89\u00b0C and 1.15\u00b0C for the phantom and ex vivo testing datasets, respectively. The DICE coefficient for the 43\u00b0C isotherm\u2010enclosed regions was 0.81, and the Bland\u2013Altman analysis indicated a bias of \u22120.25\u00b0C with limits of agreement of\u00a0\u00b12.16\u00b0C. In the 4\u2010fold under\u2010sampling case, these evaluation metrics showed approximately a 10% reduction in accuracy. Additionally, the DICE coefficient measuring the overlap between the reconstructed temperature maps (using the optimized ResUNet) and the ground truth, specifically in regions where the temperature exceeded the 43\u00b0C threshold, were 0.77 and 0.74 for the 2\u00d7 and 4\u00d7 under\u2010sampling scenarios, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates that deep learning\u2010based reconstruction significantly enhances the accuracy and efficiency of MR thermometry, particularly in the context of FUS\u2010based clinical treatments for uterine fibroids. This approach could also be extended to other applications such as essential tremor and prostate cancer treatments where MRI\u2010guided FUS plays a critical role.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17909","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T23:29:43Z","timestamp":1748734183000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Accelerated proton resonance frequency\u2010based magnetic resonance thermometry by optimized deep learning method"],"prefix":"10.1002","volume":"52","author":[{"given":"Sijie","family":"Xu","sequence":"first","affiliation":[{"name":"Biomedical Instrument Institute School of Biomedical Engineering Shanghai Jiao Tong University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenyan","family":"Zong","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for 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