Causal PETS: Causality-Informed PET Synthesis from Multi-Modal Data

Abstract

The synthesis of medical images is particularly important when certain modality data are difffcult to obtain, for example, Positron emission tomography (PET). PET is crucial for diagnosing and monitoring neurological disorders. However, the availability is limited due to factors such as high costs, radiation exposure risks, and other constraints. In this study, we propose Causal PETS, a novel causality-informed synthesis model for synthesizing PET images from multi-modal data including MRI, demographic information, and cerebrospinal fluid (CSF) biomarkers. Unlike conventional approaches that involve a straightforward conversion from T1 to PET, our model analyzes the causality between different modality data and seamlessly integrates such causality into PET image generation. Through comprehensive evaluations, we demonstrate that our Causal PETS model outperforms existing non-causal methods in terms of image clarity and accuracy, particularly in identifying regions of interest critical for neurological disorders such as Alzheimer’s Disease (AD). This work underscores the importance of causal reasoning in medical image synthesis and highlights the potential of multimodal integration to advance clinical decision making.