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Abstract #3259

Automated Segmentation of Substantia Nigra - Improved Reliability for Multiparametric MR Measurements

Ryan Hutten 1 , Nisa Desai 1 , Demetrius Maraganore 2,3 , Robert R. Edelman 1,4 , and Ying Wu 1,5

1 Radiology, Northshore University Health System, Evanston, IL, United States, 2 Northshore University Health System, IL, United States, 3 Neurology, University of Chicago, IL, United States, 4 Northwestern University Feinberg School of Medicine, IL, United States, 5 Radiology, University of Chicago, IL, United States

Sensitive and reliable measurements of the substantia nigra (SN) are imperative for early detection and follow-up of Parkinsons Disease (PD) progression. Diffusion Tensor Imaging (DTI), Magnetic Transfer Ratio (MTR) and Quantitative Susceptibility Mapping (QSM) are advanced MR modalities that have shown considerable clinical utility in PD. However these methods require labor intensive and error prone manual outlining of SN to derive quantitative measurements. Commonly used automated segmentation algorithms are currently unable to isolate the SN. We report an automated segmentation of SN and the significantly improved reliability of multiparametric MR measurements.

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