Viscoelastic biomechanical models to predict inward brain-shift using public benchmark data.

Autor:	Lesage AC; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Simmons A; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Sen A; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Singh S; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Chen M; Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Cazoulat G; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Weinberg JS; Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Brock KK; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America.
Jazyk:	angličtina
Zdroj:	Physics in medicine and biology [Phys Med Biol] 2021 Oct 12; Vol. 66 (20). Date of Electronic Publication: 2021 Oct 12.
DOI:	10.1088/1361-6560/ac22dc
Abstrakt:	Brain-shift during neurosurgery compromises the accuracy of tracking the boundaries of the tumor to be resected. Although several studies have used various finite element models (FEMs) to predict inward brain-shift, evaluation of their accuracy and efficiency based on public benchmark data has been limited. This study evaluates several FEMs proposed in the literature (various boundary conditions, mesh sizes, and material properties) by using intraoperative imaging data (the public REtroSpective Evaluation of Cerebral Tumors [RESECT] database). Four patients with low-grade gliomas were identified as having inward brain-shifts. We computed the accuracy (using target registration error) of several FEM-based brain-shift predictions and compared our findings. Since information on head orientation during craniotomy is not included in this database, we tested various plausible angles of head rotation. We analyzed the effects of brain tissue viscoelastic properties, mesh size, craniotomy position, CSF drainage level, and rigidity of meninges and then quantitatively evaluated the trade-off between accuracy and central processing unit time in predicting inward brain-shift across all models with second-order tetrahedral FEMs. The mean initial target registration error (TRE) was 5.78 ± 3.78 mm with rigid registration. FEM prediction (edge-length, 5 mm) with non-rigid meninges led to a mean TRE correction of 1.84 ± 0.83 mm assuming heterogeneous material. Results show that, for the low-grade glioma patients in the study, including non-rigid modeling of the meninges was significant statistically. In contrast including heterogeneity was not significant. To estimate the optimal head orientation and CSF drainage, an angle step of 5° and an CSF height step of 5 mm were enough leading to <0.26 mm TRE fluctuation. (© 2021 Institute of Physics and Engineering in Medicine.)
Databáze:	MEDLINE
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