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In this work, we tackle the problem of robust computed tomography (CT)reconstruction issue under a cross-domain scenario, i.e., the training CT dataas the source domain and the testing CT data as the target domain are collectedfrom different anatomical regions. Due to the mismatches of the scan region andcorresponding scan protocols, there is usually a difference of noisedistributions between source and target domains (a.k.a. noise distributionshifts), resulting in a catastrophic deterioration of the reconstructionperformance on target domain. To render a robust cross-domain CT reconstructionperformance, instead of using deterministic models (e.g., convolutional neuralnetwork), a Bayesian-endowed probabilistic framework is introduced into robustcross-domain CT reconstruction task due to its impressive robustness. Underthis probabilistic framework, we propose to alleviate the noise distributionshifts between source and target domains via implicit noise modeling schemes inthe latent space and image space, respectively. Specifically, a novel Bayesiannoise uncertainty alignment (BNUA) method is proposed to conduct implicit noisedistribution modeling and alignment in the latent space. Moreover, anadversarial learning manner is imposed to reduce the discrepancy of noisedistribution between two domains in the image space via a novel residualdistribution alignment (RDA). Extensive experiments on the head and abdomenscans show that our proposed method can achieve a better performance of robustcross-domain CT reconstruction than existing approaches in terms of bothquantitative and qualitative results.

Robust Cross-domain CT Image Reconstruction via Bayesian Noise Uncertainty Alignment