Synthetic CT generation from MRI using 3D transformer‐based denoising diffusion model
Medical Physics2023Vol. 51(4), pp. 2538–2548
Citations Over TimeTop 1% of 2023 papers
Shaoyan Pan, Elham Abouei, Jacob Wynne, Chih‐Wei Chang, Tonghe Wang, Richard L. J. Qiu, Yuheng Li, Junbo Peng, Justin Roper, Pretesh Patel, David S. Yu, Hui Mao, Xiaofeng Yang
Abstract
We have developed and validated a novel approach for generating CT images from routine MRIs using a transformer-based improved DDPM. This model effectively captures the complex relationship between CT and MRI images, allowing for robust and high-quality synthetic CT images to be generated in a matter of minutes. This approach has the potential to greatly simplify the treatment planning process for radiation therapy by eliminating the need for additional CT scans, reducing the amount of time patients spend in treatment planning, and enhancing the accuracy of treatment delivery.
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