Deep learning-based estimation of future brain atrophy using baseline MRI and PET

Motivation: Prediction of brain atrophy, a key feature in Alzheimer’s Disease (AD), is critical to observe disease progression before the onset of irreversible atrophy. Goal(s): We aim to predict future cortical atrophy rates in the elderly population from baseline PET and MRI scans. Approach: We predict image-derived cortical atrophy rate as an anatomical biomarker of neurodegeneration using image-generation deep learning networks based on T1-weighted structural MRI and PET as inputs. Results: Both T1- and PET-based models can predict longitudinal atrophy maps from baseline, with greater average atrophy in AD and mild cognitive impairment compared to cognitively normal, consistent with the Tensor-Based Morphometry method. Impact: Predicting future brain atrophy from baseline imaging can show disease progression before the onset of irreversible atrophy. Early detection of cognitive impairment and Alzheimer’s Disease progression would support planning for patient care and monitoring new lifestyle interventions and pharmacological therapies.