24 February 2017 Multi-modal and targeted imaging improves automated mid-brain segmentation
Author Affiliations +
The basal ganglia and limbic system, particularly the thalamus, putamen, internal and external globus pallidus, substantia nigra, and sub-thalamic nucleus, comprise a clinically relevant signal network for Parkinson’s disease. In order to manually trace these structures, a combination of high-resolution and specialized sequences at 7T are used, but it is not feasible to scan clinical patients in those scanners. Targeted imaging sequences at 3T such as F-GATIR, and other optimized inversion recovery sequences, have been presented which enhance contrast in a select group of these structures. In this work, we show that a series of atlases generated at 7T can be used to accurately segment these structures at 3T using a combination of standard and optimized imaging sequences, though no one approach provided the best result across all structures. In the thalamus and putamen, a median Dice coefficient over 0.88 and a mean surface distance less than 1.0mm was achieved using a combination of T1 and an optimized inversion recovery imaging sequences. In the internal and external globus pallidus a Dice over 0.75 and a mean surface distance less than 1.2mm was achieved using a combination of T1 and FGATIR imaging sequences. In the substantia nigra and sub-thalamic nucleus a Dice coefficient of over 0.6 and a mean surface distance of less than 1.0mm was achieved using the optimized inversion recovery imaging sequence. On average, using T1 and optimized inversion recovery together produced significantly improved segmentation results than any individual modality (p<0.05 wilcox sign-rank test).
Conference Presentation
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Andrew J. Plassard, Andrew J. Plassard, Pierre F. D’Haese, Pierre F. D’Haese, Srivatsan Pallavaram, Srivatsan Pallavaram, Allen T. Newton, Allen T. Newton, Daniel O. Claassen, Daniel O. Claassen, Benoit M. Dawant, Benoit M. Dawant, Bennett A. Landman, Bennett A. Landman, } "Multi-modal and targeted imaging improves automated mid-brain segmentation", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330J (24 February 2017); doi: 10.1117/12.2254428; https://doi.org/10.1117/12.2254428

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