Title: MRI2PET: Realistic PET Image Generation from MRI for Automated Inference of Brain Atrophy and Alzheimer’s
Primary area: generative models
Keywords: Machine Learning in Healthcare, Generative Modeling, Diffusion Models, Imputation

Abstract: Positron Emission Tomography (PET) is a crucial tool in medical imaging diagnostics but remains costly and less accessible than alternatives like X-Ray and MRI. To address this, we propose MRI2PET, a 3D diffusion-based model that generates AV45-PET scans from T1-weighted MRI images. MRI2PET incorporates style-transferred pre-training and a Laplacian pyramid loss to leverage unpaired MRI data and structural correspondences between modalities while simultaneously emphasizing the crucial details. Using the ADNI dataset, we demonstrate that MRI2PET produces realistic PET images and improves downstream clinical classification. Notably, augmenting the original PET-only training data with MRI2PET-synthesized scans increases AUROC from 0.688 $\pm$ 0.014 to 0.780 $\pm$ 0.005 when classifying into one of cognitively normal, mild cognitive impairment, and Alzheimer’s Disease groups. These results highlight MRI2PET's ability to generate high-quality, clinically informative PET scans from widely available MRI, offering an accessible, cost-effective approach to enhance machine learning performance and expand diagnostic imaging workflows.

TL;DR: We propose MRI2PET to generate high-quality, clinically informative PET scans from widely available MRI, offering an accessible, cost-effective approach to enhance machine learning performance and expand diagnostic imaging workflows.