{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T08:42:16Z","timestamp":1775983336240,"version":"3.50.1"},"reference-count":50,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024QF282"],"award-info":[{"award-number":["ZR2024QF282"]}],"id":[{"id":"10.13039\/501100007129","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":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The use of MRI\u2010guided radiation therapy (MRIgRT) has shown considerable advantages. However, the acquisition of electron density information still relies on Computed tomography (CT) images. In the context of medical image synthesis, achieving high global and local accuracy, as well as efficient synthetic CT (sCT) generation, remains a key challenge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to improve the accuracy of sCT image generation from MRI by introducing a novel deep learning model that leverages global context and local detail, with a minimal increase in model complexity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, we propose a novel generative adversarial network based on Mamba block and Residual Constraint (RC) strategy, namely RC\u2010MambaGAN, which achieves a significant improvement in accuracy with only a minimal increase in computation time required for sCT generation. Specifically, we integrate Mamba block into the generator to enhance the model's ability to capture long\u2010range contextual information, while preserving the precision of local features and maintaining the global structure of the image. The RC strategy improves the alignment between sCT and real CT by minimizing residuals. The model was validated on multi\u2010center datasets that included both tumor and pelvic\u2010region data. Furthermore, the dosimetric accuracy of the generated sCT images was evaluated to assess their clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>RC\u2010MambaGAN achieved high performance with mean absolute error (MAE) of 37.094\u00a0\u00b1\u00a08.761 HU, peak signal\u2010to\u2010noise ratio (PSNR) of 28.424\u00a0\u00b1\u00a01.145\u00a0dB, structural similarity index measure (SSIM) of 0.947\u00a0\u00b1\u00a00.004, and Mutual Information (MI) of 1.426\u00a0\u00b1\u00a00.012. External validation demonstrated that RC\u2010MambaGAN outperformed existing state\u2010of\u2010the\u2010art methods in both image quality and quantitative accuracy, even for structures visible in MRI but not in CT images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed RC\u2010MambaGAN markedly improves the image quality and anatomical accuracy of sCT images derived from MRI, thereby advancing the feasibility of MRI\u2010only radiotherapy workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70117","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T01:20:07Z","timestamp":1762305607000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal synthetic CT generation in tumor 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