Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson's disease via self-supervised learning with diffusion models.

Abstract

Accurate diagnosis and precise estimation of disease progression states are crucial for developing effective treatment plans for patients with parkinsonism. Although various deep learning-based computer-aided diagnostic models have demonstrated benefits, they have been relatively underexplored in parkinsonism owing to limited data and lack of external validation. We introduce the hierarchical wavelet diffusion autoencoder (HWDAE), a generative self-supervised model trained with 1,934 dopamine transporter positron emission tomography (DAT PET) images. HWDAE learns relevant disease traits during generative training, prior to supervision with human labels, as evidenced by its ability to synthesize realistic images representing different disease states of Parkinson's disease. The pretrained HWDAE is subsequently adapted for two differential diagnostic tasks and one disease progression estimation task, tested on images from two medical centers. Our training approach introduces a paradigm for deep learning research utilizing PET and expands the potential of DAT PET as a biomarker for Parkinson's disease.