TY - GEN
T1 - Clinical subthalamic nucleus prediction from high-field brain MRI
AU - Kim, Jinyoung
AU - Duchin, Yuval
AU - Sapiro, Guillermo
AU - Vitek, Jerrold
AU - Harel, Noam
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/21
Y1 - 2015/7/21
N2 - The subthalamic nucleus (STN) within the sub-cortical region of the Basal ganglia is a crucial targeting structure for Parkinson's Deep brain stimulation (DBS) surgery. Volumetric segmentation of such small and complex structure, which is elusive in clinical MRI protocols, is thereby a pre-requisite process for reliable DBS direct targeting. While direct visualization of the STN is facilitated with advanced ultrahigh-field MR imaging (7 Tesla), such high fields are not always clinically available. In this paper, we aim at the automatic prediction of the STN region on clinical low-field MRI, exploiting dependencies between the STN and its adjacent structures, learned from ultrahigh-field MRI. We present a framework based on a statistical shape model to learn such shape relationship on high quality MR data sets. This allows for an accurate prediction and visualization of the STN structure, given detectable predictors on the low-field MRI. Experimental results on Parkinson's patients demonstrate that the proposed approach enables accurate estimation of the STN on clinical 1.5T MRI.
AB - The subthalamic nucleus (STN) within the sub-cortical region of the Basal ganglia is a crucial targeting structure for Parkinson's Deep brain stimulation (DBS) surgery. Volumetric segmentation of such small and complex structure, which is elusive in clinical MRI protocols, is thereby a pre-requisite process for reliable DBS direct targeting. While direct visualization of the STN is facilitated with advanced ultrahigh-field MR imaging (7 Tesla), such high fields are not always clinically available. In this paper, we aim at the automatic prediction of the STN region on clinical low-field MRI, exploiting dependencies between the STN and its adjacent structures, learned from ultrahigh-field MRI. We present a framework based on a statistical shape model to learn such shape relationship on high quality MR data sets. This allows for an accurate prediction and visualization of the STN structure, given detectable predictors on the low-field MRI. Experimental results on Parkinson's patients demonstrate that the proposed approach enables accurate estimation of the STN on clinical 1.5T MRI.
KW - Deep brain stimulation
KW - high-field MRI
KW - statistical shape models
KW - subthalamic nucleus
UR - http://www.scopus.com/inward/record.url?scp=84944318408&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944318408&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7164104
DO - 10.1109/ISBI.2015.7164104
M3 - Conference contribution
AN - SCOPUS:84944318408
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1264
EP - 1267
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -