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.

使用具有转换器的漫反射概率模型（MC-DDPM）将磁共振成像（MRI）转换为高质量的合成计算机断层扫描（sCT），以便于放射治疗计划，并通过消除 CT 模拟和具有误差的图像配准，从而减少患者的辐射剂量和设置不确定性。

基于 3D 变压器去噪扩散模型的合成磁共振成像计算机断层成像

Synthetic CT Generation from MRI using 3D Transformer-based Denoising  Diffusion Model

Background: Synthetic computed tomography (sCT) has been proposed and
increasingly clinically adopted to enable magnetic resonance imaging
(MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the
ability to generate accurate sCT from fixed MRI acquisitions. However, MRI
protocols may change over time or differ between centres resulting in
low-quality sCT due to poor model generalisation. Purpose: investigating domain
randomisation (DR) to increase the generalisation of a DL model for brain sCT
generation. Methods: CT and corresponding T1-weighted MRI with/without
contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were
collected, considering FLAIR the unseen sequence where to investigate
generalisation. A ``Baseline'' generative adversarial network was trained
with/without the FLAIR sequence to test how a model performs without DR. Image
similarity and accuracy of sCT-based dose plans were assessed against CT to
select the best-performing DR approach against the Baseline. Results: The
Baseline model had the poorest performance on FLAIR, with mean absolute error
(MAE)=106$\pm$20.7 HU (mean$\pm\sigma$). Performance on FLAIR significantly
improved for the DR model with MAE=99.0$\pm$14.9 HU, but still inferior to the
performance of the Baseline+FLAIR model (MAE=72.6$\pm$10.1 HU). Similarly, an
improvement in $\gamma$-pass rate was obtained for DR vs Baseline. Conclusions:
DR improved image similarity and dose accuracy on the unseen sequence compared
to training only on acquired MRI. DR makes the model more robust, reducing the
need for re-training when applying a model on sequences unseen and unavailable
for retraining.

研究领域：通过深度学习及领域随机化技术生成高质量头部合成计算机断层扫描图像，提高医疗影像辅助治疗的精度。