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Proceedings Paper

Validation of model-based brain shift correction in neurosurgery via intraoperative magnetic resonance imaging: preliminary results
Author(s): Ma Luo; Sarah F. Frisken; Jared A. Weis; Logan W. Clements; Prashin Unadkat; Reid C. Thompson; Alexandra J. Golby; Michael I. Miga
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Paper Abstract

The quality of brain tumor resection surgery is dependent on the spatial agreement between preoperative image and intraoperative anatomy. However, brain shift compromises the aforementioned alignment. Currently, the clinical standard to monitor brain shift is intraoperative magnetic resonance (iMR). While iMR provides better understanding of brain shift, its cost and encumbrance is a consideration for medical centers. Hence, we are developing a model-based method that can be a complementary technology to address brain shift in standard resections, with resource-intensive cases as referrals for iMR facilities. Our strategy constructs a deformation ‘atlas’ containing potential deformation solutions derived from a biomechanical model that account for variables such as cerebrospinal fluid drainage and mannitol effects. Volumetric deformation is estimated with an inverse approach that determines the optimal combinatory ‘atlas’ solution fit to best match measured surface deformation. Accordingly, preoperative image is updated based on the computed deformation field. This study is the latest development to validate our methodology with iMR. Briefly, preoperative and intraoperative MR images of 2 patients were acquired. Homologous surface points were selected on preoperative and intraoperative scans as measurement of surface deformation and used to drive the inverse problem. To assess the model accuracy, subsurface shift of targets between preoperative and intraoperative states was measured and compared to model prediction. Considering subsurface shift above 3 mm, the proposed strategy provides an average shift correction of 59% across 2 cases. While further improvements in both the model and ability to validate with iMR are desired, the results reported are encouraging.

Paper Details

Date Published: 3 March 2017
PDF: 11 pages
Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 1013503 (3 March 2017); doi: 10.1117/12.2255845
Show Author Affiliations
Ma Luo, Vanderbilt Univ. (United States)
Sarah F. Frisken, Brigham and Women's Hospital (United States)
Jared A. Weis, Vanderbilt Univ. (United States)
Logan W. Clements, Vanderbilt Univ. (United States)
Prashin Unadkat, Brigham and Women's Hospital (United States)
Reid C. Thompson, Vanderbilt Univ. Medical Ctr. (United States)
Alexandra J. Golby, Brigham and Women's Hospital (United States)
Michael I. Miga, Vanderbilt Univ. (United States)
Vanderbilt Univ. Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 10135:
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster III; Baowei Fei, Editor(s)

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