{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T03:23:06Z","timestamp":1783740186314,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Deanship at University of Ha\u2019il","award":["RG-24 182"],"award-info":[{"award-number":["RG-24 182"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The clinical imperative to reduce patient ionizing radiation exposure during diagnosis and treatment planning necessitates robust, high-fidelity synthetic imaging solutions. Current cross-modal synthesis techniques, primarily based on GANs and deterministic CNNs, exhibit instability and critical errors in modeling high-contrast tissues, thereby hindering their reliability for safety-critical applications such as radiotherapy. Objectives: Our primary objective was to develop a stable, high accuracy framework for 3D Magnetic Resonance Imaging (MRI) to Computed Tomography (CT) synthesis capable of generating clinically equivalent synthetic CTs (sCTs) across multiple anatomical sites. Methods: We introduce a novel 3D Latent Diffusion Model (3DLDM) that operates in a compressed latent space, mitigating the computational burden of 3D diffusion while leveraging the stability of the denoising objective. Results: Across the Head &amp; Neck, Thorax, and Abdomen, the 3DLDM achieved a Mean Absolute Error (MAE) of 56.44 Hounsfield Units (HU). This result demonstrates a significant 3.63% reduction in overall error compared to the strongest adversarial baseline, CycleGAN (MAE = 60.07 HU, p &lt; 0.05), a 10.76% reduction compared to NNUNet (MAE = 67.20 HU, p &lt; 0.01), and a 20.79% reduction compared to the transformer-based SwinUNeTr (MAE = 77.23 HU, p &lt; 0.0001). The model also achieved the highest structural similarity (SSIM = 0.885 \u00b1 0.031), significantly exceeding SwinUNeTr (p &lt; 0.0001), NNUNet (p &lt; 0.01), and Pix2Pix (p &lt; 0.0001). Likewise, the 3D-LDM achieved the highest peak signal-to-noise ratio (PSNR = 29.73 \u00b1 1.60 dB), with statistically significant gains over CycleGAN (p &lt; 0.01), NNUNet (p &lt; 0.001), and SwinUNeTr (p &lt; 0.0001). Conclusions: This work validates a scalable, accurate approach for volumetric synthesis, positioning the 3DLDM to enable MR-only radiotherapy planning and accelerate radiation-free multi-modal imaging in the clinic.<\/jats:p>","DOI":"10.3390\/diagnostics15233010","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T14:39:43Z","timestamp":1764167983000},"page":"3010","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["3D Latent Diffusion Model for MR-Only Radiotherapy: Accurate and Consistent Synthetic CT Generation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0703-5826","authenticated-orcid":false,"given":"Mohammed A.","family":"Mahdi","sequence":"first","affiliation":[{"name":"Information and Computer Science Department, College of Computer Science and Engineering, University of Ha\u2019il, Ha\u2019il 55476, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2999-8516","authenticated-orcid":false,"given":"Mohammed","family":"Al-Shalabi","sequence":"additional","affiliation":[{"name":"Information and Computer Science Department, College of Computer Science and Engineering, University of Ha\u2019il, Ha\u2019il 55476, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6570-0989","authenticated-orcid":false,"given":"Ehab T.","family":"Alnfrawy","sequence":"additional","affiliation":[{"name":"Information Security Department, College of Computer Science and Engineering, University of Ha\u2019il, Ha\u2019il 55476, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reda","family":"Elbarougy","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Data Science Department, College of Computer Science and Engineering, University of Ha\u2019il, Ha\u2019il 55476, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3363-2886","authenticated-orcid":false,"given":"Muhammad Usman","family":"Hadi","sequence":"additional","affiliation":[{"name":"School of Engineering, Ulster University, Belfast BT15 1AP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0701-6761","authenticated-orcid":false,"given":"Rao Faizan","family":"Ali","sequence":"additional","affiliation":[{"name":"School of Computing, University of Kent, Canterbury CT2 7NZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"ref_1","unstructured":"Kitson, S.L. 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