  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. We propose an MRI-to-CT transformer-based
denoising diffusion probabilistic model (MC-DDPM) to transform MRI into
high-quality sCT to facilitate radiation treatment planning. MC-DDPM 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
adds Gaussian noise to real CT scans, 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 patient to produce noise-free CT scans.
With an optimally trained Swin-Vnet, the reverse diffusion process was used to
generate sCT scans matching MRI anatomy. We evaluated the proposed method by
generating sCT from MRI on a brain dataset and a prostate dataset. Qualitative
evaluation was performed using the mean absolute error (MAE) of Hounsfield unit
(HU), peak signal to noise ratio (PSNR), multi-scale Structure Similarity index
(MS-SSIM) and normalized cross correlation (NCC) indexes between ground truth
CTs and sCTs. MC-DDPM generated brain sCTs with state-of-the-art quantitative
results with MAE 43.317 HU, PSNR 27.046 dB, SSIM 0.965, and NCC 0.983. For the
prostate dataset, MC-DDPM achieved MAE 59.953 HU, PSNR 26.920 dB, SSIM 0.849,
and NCC 0.948. In conclusion, we have developed and validated a novel approach
for generating CT images from routine MRIs using a transformer-based DDPM. This
model effectively captures the complex relationship between CT and MRI images,
allowing for robust and high-quality synthetic CT (sCT) images to be generated
in minutes.
