Tau Pet Harmonization via Surface-Based Diffusion Model

The heterogeneity inherent in tau positron emission tomog-raphy (PET) imaging data across different tracers challenges the integration of multi-site tau PET data, thereby necessitating the trustful harmonization technique for a better utilization of the emerging large-scale datasets. Unlike other imaging modalities, the harmonization among multi-site tau PET data involves more than intensity mapping but contains in-tricate pattern alterations attributed to tracer binding proper-ties, which makes the existing statistical methods inadequate. Meanwhile, the effective data preprocessing is required to eliminate the artifacts caused by off-target binding and partial volume effect for meaningful comparison and harmonization. In this paper, we propose a systematic tau PET harmonization framework that involves the surface-based data preprocessing and diffusion model for generating the vertex-wise mapping between multi-site tau standardized uptake value ra-tio (SUVR) on the cortical surface. In the experiments, using large-scale Alzheimer's Disease Neuroimaging Initiative (ADNI) and Health and Aging Brain Study-Health Dispari-ties (HABS-HD) data with different tracers, we demonstrate our method can successfully achieve harmonization by gen-erating the SUVR maps with consistent pattern distributions and persevering the individual variability.