Abstract #4711

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

Punith B Venkategowda^1,2, Tommaso Di Noto^3,4,5, Ricardo Corredor-Jerez^3,4,5, Tobias Bodenmann³, Madappa Shadakshari Swamy¹, Bhairav Mehta¹, Alessandra Griffa⁶, Sandrine Nadeau⁶, Gilles Allali⁶, Ling Ling Chan^7,8, Vincent Dunet⁵, Jitender Saini⁹, Max Scheffler¹⁰, Neelam Sinha², and Bénédicte Maréchal^3,4,5

¹Siemens Healthineers India, Bengaluru, India, Bengaluru, India, ²International Institute of Information Technology Bangalore, Bengaluru, India, ³Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, ⁴LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁵Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁶Leenaards Memory Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁷Singapore General Hospital, Singapore, Singapore, ⁸Duke-NUS Medical School, Singapore, Singapore, ⁹National Institute of Mental Health and Neurosciences, Bengaluru, India, ¹⁰Division of Radiology, Geneva University Hospitals, Geneva, Switzerland

Synopsis

Keywords: Diagnosis/Prediction, Neurodegeneration, MRPI, Progressive supranuclear palsy, Parkinsonism Index, CAD

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.

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