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Magnetic resonance imaging (MRI)‐based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error‐prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI‐to‐CT transformer‐based improved denoising diffusion probabilistic model (MC‐IDDPM) to translate MRI into high‐quality sCT to facilitate radiation treatment planning.

MC‐IDDPM implements diffusion processes with a shifted‐window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process, which involves adding Gaussian noise to real CT scans to create noisy images, and a reverse process, in which a shifted‐window transformer V‐net (Swin‐Vnet) denoises the noisy CT scans conditioned on the MRI from the same …