MRI-Styled PET: A Dual Modality Fusion Approach to PET Partial Volume Correction

Abstract

Positron emission tomography (PET) with [18F]-fludeoxyglucose (FDG) can visualize the spatial pattern of neurodegeneration-related glucose hypometabolism. We proposed the “MRI-styled PET,” leveraging anatomical information from T1-weighted magnetic resonance imaging to enhance the structural details and quantitative accuracy of FDG-PET, which is degraded by partial volume effects (PVE). The proposed framework comprised a baseline encoder-decoder image fusion model and several task-specific modules; notably, the alternative anatomical input significantly contributes to correcting the under/overestimation of gray/white matter while the adaptive multiscale structural similarity loss utilized learnable ratios across various receptive fields to modulate attention to tissue contrast. Compared to a traditional anatomy-guided post-reconstruction PVE correction method (PVC-PET), MRI-styled PET demonstrated significantly higher structural similarity and peak signal-to-noise ratio than the baseline image fusion model (Baseline), showcasing the effectiveness of the proposed task-specific modules. In several Alzheimer’s Disease-related brain regions, MRI-styled PET exhibited consistent increases in corrective effects regardless of disease stage, compared to Baseline and PVC-PET. In conclusion, this study represented an initial exploration of a deep-learning approach for correcting PVE in PET without prior knowledge regarding the correction method or the underlying radiotracer uptake and without assumptions about the system point-spread function. Our implementation is available at https://github.com/NTUMMIO/MRI-styled-PET.