Synthetic CT generation from MRI using 3D diffusion model

Shaoyan Pan; Elham Abouei; Jacob Wynne; Tonghe Wang; Richard L. J. Qiu; Yuheng Li; Chih-Wei Chang; Junbo Peng; Shihan Qiu; Justin Roper; Pretesh Patel; David S. Yu; Hui Mao; Xiaofeng Yang

doi:10.1117/12.3006578

2 April 2024 Synthetic CT generation from MRI using 3D diffusion model

Shaoyan Pan, Elham Abouei, Jacob Wynne, Tonghe Wang, Richard L. J. Qiu, Yuheng Li, Chih-Wei Chang, Junbo Peng, Shihan Qiu, Justin Roper, Pretesh Patel, David S. Yu, Hui Mao, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129262L (2024) https://doi.org/10.1117/12.3006578
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

This study aims to simplify radiation therapy treatment planning by proposing an MRI-to-CT transformer-based denoising diffusion probabilistic model (CT-DDPM) to generate high-quality synthetic computed tomography (sCT) from magnetic resonance imaging (MRI). The goal is to reduce patient radiation dose and setup uncertainty by eliminating the need for CT simulation and image registration during treatment planning. The CT-DDPM utilizes a diffusion process with a shifted-window transformer network to transform MRI into sCT. The model comprises two processes: a forward process, adding Gaussian noise to real CT scans to create noisy images, and a reverse process, denoising the noisy CT scans using a Vshaped network (Vnet) conditioned on the corresponding MRI. With an optimally trained Swin-Vnet, the reverse process generates sCT scans matching the MRI anatomy. The method is evaluated using mean absolute error (MAE) of Hounsfield unit (HU), peak signal-to-noise ratio (PSNR), multi-scale Structural Similarity index (MS-SSIM) and normalized cross-correlation (NCC) between ground truth CTs and sCTs. For the brain dataset, CT-DDPM demonstrated state-of-the-art quantitative results, exhibiting an MAE of 45.210±3.807 HU, a PSNR of 26.753±0.861 dB, an SSIM of 0.964±0.005, and an NCC of 0.981±0.004. In the context of the prostate dataset, the model also showed impressive performance with an MAE of 55.492±8.281 HU, a PSNR of 28.912±2.591 dB, an SSIM of 0.894±0.092, and an NCC of 0.945±0.054. Across both datasets, CT-DDPM significantly outperformed competing networks in most metrics, a finding corroborated by the student’s paired t-test. The source code is available: https://github.com/shaoyanpan/Synthetic-CT-generation-from- MRI-using-3D-transformer-based-denoising-diffusion-model

Citation Download Citation

Shaoyan Pan, Elham Abouei, Jacob Wynne, Tonghe Wang, Richard L. J. Qiu, Yuheng Li, Chih-Wei Chang, Junbo Peng, Shihan Qiu, Justin Roper, Pretesh Patel, David S. Yu, Hui Mao, and Xiaofeng Yang "Synthetic CT generation from MRI using 3D diffusion model", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262L (2 April 2024); https://doi.org/10.1117/12.3006578

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