{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:49:21Z","timestamp":1778820561602,"version":"3.51.4"},"reference-count":57,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T00:00:00Z","timestamp":1701561600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["WK2030000037"],"award-info":[{"award-number":["WK2030000037"]}],"id":[{"id":"10.13039\/501100012226","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":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Cine magnetic resonance (MR) images have been used for real\u2010time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low\u2010field strength face the problem of limited image quality.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study employed MR images from 18 pancreatic cancer patients who received MR\u2010guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning\u2010based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal\u2010to\u2010noise ratio (SNR), structural similarity index measure, peak SNR, blind\/reference\u2010less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception\u2010based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Significant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20\u00a0ms for a typical input image size of 512 \u00d7 512.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Taking advantage of the prior high\u2010quality positioning and planning MR images, the deep learning\u2010based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real\u2010time MRgRT.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16860","type":"journal-article","created":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T13:30:35Z","timestamp":1701610235000},"page":"3495-3509","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI\u2010guided radiation therapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Yuyan","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Engineering and Applied Physics University of Science and Technology of China  Hefei Anhui 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