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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Vydáno v:	Medical physics (Lancaster) Ročník 51; číslo 4; s. 2538 - 2548
Hlavní autoři:	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:	angličtina
Vydáno:	United States 01.04.2024
Témata:	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
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Shrnutí:	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 work, we propose a MRI‐to‐CT transformer‐based improved denoising diffusion probabilistic model (MC‐IDDPM) to translate MRI into high‐quality sCT to facilitate radiation treatment planning. Methods MC‐IDDPM 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 involves adding Gaussian noise to real CT scans to create noisy images, 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 noise‐free sCT scans matching MRI anatomy. We evaluated the proposed method by generating sCT from MRI on an institutional brain dataset and an institutional prostate dataset. Quantitative evaluations were conducted using several metrics, including Mean Absolute Error (MAE), Peak Signal‐to‐Noise Ratio (PSNR), Multi‐scale Structure Similarity Index (SSIM), and Normalized Cross Correlation (NCC). Dosimetry analyses were also performed, including comparisons of mean dose and target dose coverages for 95% and 99%. Results MC‐IDDPM generated brain sCTs with state‐of‐the‐art quantitative results with MAE 48.825 ± 21.491 HU, PSNR 26.491 ± 2.814 dB, SSIM 0.947 ± 0.032, and NCC 0.976 ± 0.019. For the prostate dataset: MAE 55.124 ± 9.414 HU, PSNR 28.708 ± 2.112 dB, SSIM 0.878 ± 0.040, and NCC 0.940 ± 0.039. MC‐IDDPM demonstrates a statistically significant improvement (with p < 0.05) in most metrics when compared to competing networks, for both brain and prostate synthetic CT. Dosimetry analyses indicated that the target dose coverage differences by using CT and sCT were within ± 0.34%. Conclusions 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0094-2405 2473-4209 2473-4209
DOI:	10.1002/mp.16847