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Computer‐assisted automatic localization of the human pedunculopontine nucleus in T1‐weighted MR images: a preliminary study
Author(s) -
Fu Yili,
Gao Wenpeng,
Zhu Minwei,
Chen Xiaoguang,
Lin Zhiguo,
Wang Shuguo
Publication year - 2009
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.262
Subject(s) - pedunculopontine nucleus , brainstem , computer science , pedunculopontine tegmental nucleus , artificial intelligence , fully automatic , deep brain stimulation , pattern recognition (psychology) , neuroscience , parkinson's disease , medicine , psychology , pathology , disease , mechanical engineering , engineering
Background The pedunculopontine nucleus (PPN) is a new promising target of deep brain stimulation (DBS) for the treatment of Parkinson's disease. This study was to develop a method of computer‐assisted automatic localization of the PPN in T1‐weighted MR images. Methods A 3D template of a segment of the brainstem containing the PPN was constructed. A knowledge‐based, hierarchical method of template‐to‐subject registration was proposed to register the template to the subject's data to locate the subject's PPN. Results Experiments were performed with both T1‐weighted and proton density MR images acquired from 12 people. Preliminary results show that the proposed method can locate the PPN with an error of 1.83 ± 0.42 mm for its rostral pole and 1.57 ± 0.34 mm for its caudal pole. Conclusion The proposed method is automatic, robust and accurate in the localization of the PPN, which demonstrates utility for preoperative surgical planning. Copyright © 2009 John Wiley & Sons, Ltd.