Ms. Shihan Qiu Profile

Shihan Qiu

at Univ of California Los Angeles

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Proceedings Article | 2 April 2024 Poster + Paper

Synthetic CT generation from MRI using 3D diffusion model

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

Proceedings Volume 12926, 129262L (2024) https://doi.org/10.1117/12.3006578

KEYWORDS: Computed tomography, Magnetic resonance imaging, Diffusion, 3D modeling, Denoising, Brain, Prostate, Education and training, Radiotherapy, Lithium

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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

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