Demystifying T1-MRI to FDG\(^{18}\)-PET Image Translation via Representational Similarity

Recent development of image-to-image translation techniques has enabled the generation of rare medical images (e.g., PET) from common ones (e.g., MRI). Beyond the potential benefits of the reduction in scanning time, acquisition cost, and radiation exposure risks, the translation models in themselves are inscrutable black boxes. In this work, we propose two approaches to demystify the image translation process, where we particularly focus on the T1-MRI to PET translation. First, we adopt the representational similarity analysis and discover that the process of T1-MR to PET image translation includes the stages of brain tissue segmentation and brain region recognition, which unravels the relationship between the structural and functional neuroimaging data. Second, based on our findings, an Explainable and Simplified Image Translation (ESIT) model is proposed to demonstrate the capability of deep learning models for extracting gray matter volume information and identifying brain regions related to normal aging and Alzheimer’s disease, which untangles the biological plausibility hidden in deep learning models.