Trustworthy Contrast-enhanced Brain MRI Synthesis

Contrast-enhanced brain MRI (CE-MRI) is a valuable diagnostic technique but may pose health risks and incur high costs. To create safer alternatives, multi-modality medical image translation aims to synthesize CE-MRI images from other available modalities. Although existing methods can generate promising predictions, they still face two challenges, i.e., exhibiting over-confidence and lacking interpretability on predictions. To address the above challenges, this paper introduces TrustI2I, a novel trustworthy method that reformulates multi-to-one medical image translation problem as a multimodal regression problem, aiming to build an uncertainty-aware and reliable system. Specifically, our method leverages deep evidential regression to estimate prediction uncertainties and employs an explicit intermediate and late fusion strategy based on the Mixture of Normal Inverse Gamma (MoNIG) distribution, enhancing both synthesis quality and interpretability. Additionally, we incorporate uncertainty calibration to improve the reliability of uncertainty. Validation on the BraTS2018 dataset demonstrates that our approach surpasses current methods, producing higher-quality images with rational uncertainty estimation.