Multimodal Generative Modeling for DaT Scan Reconstruction in Parkinson's Disease

The creation of synthetic medical data that truly captures the statistical distribution of real-world patient information, while simultaneously protecting individual privacy, remains a formidable challenge for the clinical and scientific community. This challenge is especially pronounced in nuclear medicine research, where rigorous data sharing is hindered by tight regulations and ethical considerations. In this study, we introduce a multimodal deep learning model designed to reconstruct (and thereby enable future synthesis of) 123I-FPCIT SPECT (DaT) scans by leveraging both the target DaT and co-registered T1-weighted MRI scans. Through extensive experimentation on a large Parkinson's Progression Markers Initiative (PPMI) dataset, comprising healthy controls, Parkinson's disease (PD) patients, and individuals without imaging evidence of dopaminergic deficits (SWEDD), we demonstrate that the proposed framework yields DaT images that strongly preserve clinical signal distributions. Our findings show minimal intensity discrepancies, unbiased contrast-to-noise ratios, and robust region-based analyses across pathological and demographic subgroups, highlighting the feasibility of this approach for large-scale data augmentation in neurodegenerative research.Clinical Relevance-By enabling the reconstruction of DaT scans from multi-contrast inputs, this framework has the potential to enhance future generation of synthetic data that are applicable to early PD detection, disease progression studies, and model training in clinical scenarios where data are often limited, and privacy constraints are stringent.