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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