CNN-based Automated Pipeline for Accurate Computation of Magnetic Resonance Parkinsonism’s Index Measurements

Motivation: Magnetic resonance parkinsonism index (MRPI) has shown promising results in differentiating progressive supranuclear palsy from idiopathic Parkinson’s disease and the Parkinson variant of multiple system atrophy (MSA-P). Goal(s): In this work, we propose a fully automated pipeline to calculate MRPI using a convolutional neural network (CNN). This can be a time-saving tool in making diagnoses in clinically ambiguous cases. Approach: Our method utilizes registration and deep learning-based segmentation techniques to extract relevant measurements from T1 weighted MRI images (T1w). Results: Experimental results demonstrated the robustness of our approach and its generalizability across different clinical settings. Impact: Automating the measurement of MRPI components with a deep learning based algorithm can help providing objective and reproducible measures. It may be beneficial for differential diagnosis of patients with Parkinsonian syndromes with significant savings in reporting time.