Multimodal HYPR-based Denoising for Improving Brain Pattern Analysis

Brain pattern analysis is an exploratory yet powerful method for deriving disease-related patterns that function as imaging biomarkers. Given that multiple functional systems may be affected in disease, pattern analysis applied to multimodal imaging datasets provides an increasingly detailed view of pathophysiology. Voxelwise pattern analysis is a purely data-driven method for identifying which regions of the brain are most affected. Given the high voxel noise in these datasets, aggressive spatial filtering is usually employed before analysis. However, it has yet to be investigated whether more sophisticated, resolution-preserving denoising operators, such as HYPR, can improve the precision, interpretability, and robustness of identified patterns. As a foray into these questions, we apply HYPR-based denoising to a small pilot FDG-PET/fMRI dataset of Parkinson’s disease subjects before applying pattern analysis. FDG data are reconstructed using PSF-HYPR4D-K-TOFOSEM and fMRI data are processed with a proposed novel 4D HYPR operator. PET denoising reduces noise-induced variation by up to 40% and HYPR-based fMRI denoising enables voxelwise joint analysis of the PET and fMRI data, allowing one to analyze patterns of accordance and discordance from the functional metrics of each modality. Applying voxelwise pattern analysis to the denoised dataset versus traditional aggressive spatial filtering minimizes the amount of resolution loss, allowing for subregional pattern structure to be more precisely characterized. Therefore, more sophisticated denoising provides truly voxelwise, more interpretable patterns, which may have downstream effects on disease understanding and treatment.