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Although deep learning (DL)\u2010based synthetic computed tomography (CT) generation models have been developed to obtain CT density information from MRI, they usually do not meet the requirement of real\u2010time plan adaptation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We propose a DL\u2010based photon dose calculation method directly on 0.35\u00a0T MRI to skip synthetic CT generation and show its feasibility for prostate patient\u00a0cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The 0.35 T planning MRI and deformed planning CT (registered to the planning MRI) of 34 prostate cancer patients treated with a 0.35 T magnetic resonance\u2010linear accelerator (MR\u2010Linac) were collected. The air cavities (ACs) in the abdominopelvic area of the deformed CT images were corrected based on manual AC contouring on the MRI. Monte Carlo (MC) dose simulations under a 0.35 T magnetic field were performed on the corrected CT images. All photon beams were simulated using a uniform field size of . 10\u00a0800 beams were simulated with  initial photons for training (20 patients) and 2160 beams with  photons for validation (4 patients). For testing, 1080 beams shooting through the planning target volume (PTV) in 10 patients and five optimized nine\u2010field intensity\u2010modulated plans were simulated with  photons. 3D MRI cuboids covering the photon beams were input into a Unet model to predict AC segmentation, and 3D MRI and predicted AC cuboids were input into a long short\u2010term memory (LSTM) model for beam's eye view (BEV) processing to predict dose. The gamma passing rate  (2%\/2mm, ), beam dose profiles of single beams and dose volume histogram (DVH) of intensity\u2010modulated plans were evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The test results for all photon beams from the proposed models demonstrated a mean  above 99.50%. The five treatment plans recalculated by the DL model each achieved  values exceeding 99.80%. Additionally, the model's inference time was approximately 12 ms per photon\u00a0beam.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed method showed that DL\u2010based dose calculation directly on MRI is feasible for prostate cases, which has the potential to simplify the procedure for MRI\u2010only workflows and can be beneficial for real\u2010time plan\u00a0adaptation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70106","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T19:12:39Z","timestamp":1762283559000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning\u2010based synthetic\u2010CT\u2010free photon dose calculation in MR\u2010guided radiotherapy: A proof\u2010of\u2010concept 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