Masked Residual Diffusion Probabilistic Model with Regional Asymmetry Prior for Generating Perfusion Maps from Multi-phase CTA

Multiphase CT angiography (mCTA) has become an important diagnostic tool for acute ischemic stroke (AIS), offering insights into occlusion sites and collateral circulation. However, its broader application is hindered by the need for specialized interpretation, contrasting with the intuitive nature of CT perfusion (CTP). In this work, we propose a novel diffusion based generative model to generate CTP-like perfusion maps, enhancing AIS diagnosis in resource-limited settings. Unlike traditional diffusion models that restore images by predicting the added noise, our approach uses a masked residual diffusion probabilistic model (MRDPM) to recover the residuals between the predicted and target image within brain regions of interests for more detailed generation. To target denoising efforts on relevant regions, noise is selectively added into the brain area only during diffusion. Furthermore, a Multi-scale Asymmetry Prior module and a Brain Region-Aware Network are proposed to incorporate anatomical prior information into the MRDPM to generate finer details while ensuring consistency. Experimental evaluations with 514 patient images demonstrate that our proposed method is able to generate high quality CTP-like perfusion maps, outperforming several other generative models regarding the metrics of MAE, LPIPS, SSIM, and PSNR. The code is publicly available at https://github.com/UniversalCAI/MRDPM-with-RAP .