Synthetic CT generation from MRI using 3D transformer‐based denoising diffusion model

Background and purpose 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. In this wo...

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Vydané v:	Medical physics (Lancaster) Ročník 51; číslo 4; s. 2538 - 2548
Hlavní autori:	Pan, Shaoyan, Abouei, Elham, Wynne, Jacob, Chang, Chih‐Wei, Wang, Tonghe, Qiu, Richard L. J., Li, Yuheng, Peng, Junbo, Roper, Justin, Patel, Pretesh, Yu, David S., Mao, Hui, Yang, Xiaofeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.04.2024
Predmet:	deep learning diffusion model Head Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging - methods Male MRI Radiometry Radiotherapy Planning, Computer-Assisted - methods synthetic CT Tomography, X-Ray Computed - methods transformer transformer deep learning synthetic CT diffusion model MRI
ISSN:	0094-2405, 2473-4209, 2473-4209
On-line prístup:	Získať plný text
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