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.

中文翻译：

---

使用基于 3D 变压器的去噪扩散模型从 ​​MRI 生成合成 CT

基于磁共振成像 (MRI) 的合成计算机断层扫描 (sCT) 通过消除 CT 模拟和容易出错的图像配准的需要简化了放射治疗的治疗计划，最终减少了患者的辐射剂量和设置的不确定性。我们提出了一种基于 MRI-to-CT 变压器的去噪扩散概率模型 (MC-DDPM)，以将 MRI 转换为高质量的 sCT，以促进放射治疗计划。MC-DDPM 使用移动窗口变换器网络实施扩散过程，以从 MRI 生成 sCT。所提出的模型由两个过程组成：一个正向过程，将高斯噪声添加到真实的 CT 扫描中，一个反向过程，其中一个移动窗口变换器 V-net (Swin-Vnet) 对来自 MRI 的噪声 CT 扫描进行去噪。同一患者产生无噪声 CT 扫描。使用经过优化训练的 Swin-Vnet，反向扩散过程用于生成与 MRI 解剖结构匹配的 sCT 扫描。我们通过在大脑数据集和前列腺数据集上从 MRI 生成 sCT 来评估所提出的方法。使用Hounsfield单位（HU）的平均绝对误差（MAE）、峰值信噪比（PSNR）、多尺度结构相似性指数（MS-SSIM）和ground truth之间的归一化互相关（NCC）指数进行定性评价CT 和 sCT。MC-DDPM 生成的大脑 sCT 具有最先进的定量结果，MAE 43.317 HU、PSNR 27.046 dB、SSIM 0.965 和 NCC 0.983。对于前列腺数据集，MC-DDPM 实现了 MAE 59.953 HU、PSNR 26.920 dB、SSIM 0.849 和 NCC 0.948。综上所述，我们开发并验证了一种使用基于变压器的 DDPM 从常规 MRI 生成 CT 图像的新方法。该模型有效地捕获了 CT 和 MRI 图像之间的复杂关系，从而可以在几分钟内生成稳健且高质量的合成 CT (sCT) 图像。