Automated Deep Learning-Based Magnetic Resonance Parkinsonism Index 2.0 in Early Parkinson’s Disease: A Longitudinal Study

Motivation: There is a pressing need for accurate and reliable methods to track disease progression in patients presenting with early Parkinsonism. Goal(s): We aim to assess the utility of a fully automated deep learning-based method developed to estimate both MRPI 1.0 and 2.0 measures in a large, longitudinal, three time-point case-control cohort of patients presenting with early Parkinsonism. Approach: MRPI 1.0 and 2.0 measures were computed from 3D T1-weighted images using the Quantitative Brain Assessment Toolkit (QBAT, v2.1.0) research application. Results: MRPI 2.0 showed improved group differentiation and disease classification when compared to MRPI 1.0 in patients presenting with early Parkinsonism. Impact: Automated, deep learning-based MRPI 2.0 assessment may be used as quick tool to facilitate radiological screening, complementary to other quantitative MRI techniques such as quantitative susceptibility mapping and diffusion MRI, to track progression in Parkinson’s disease.