A Sparse Intraoperative Data-Driven Biomechanical Model to Compensate for Brain Shift during Neuronavigation

• Published in: American journal of neuroradiology : AJNR Vol. 32; no. 2; pp. 395 - 402
• Main Authors: ZHUANG, D.-X, LIU, Y.-X, WU, J.-S, YAO, C.-J, MAO, Y, ZHANG, C.-X, WANG, M.-N, WANG, W, ZHOU, L.-F
• Format: Journal Article
• Language: English
• Published: Oak Brook, IL American Society of Neuroradiology 01.02.2011
• Subjects: Adolescent; Adult; Aged; Algorithms; Biological and medical sciences; Biomechanical Phenomena; Brain; Brain - physiology; Brain - surgery; Child; Craniotomy; Elasticity; Electrodiagnosis. Electric activity recording; Female; Humans; Intraoperative Period; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Middle Aged; Models, Biological; Nervous system; Neuronavigation; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Young Adult; Intraoperative; Nervous system diseases; Models; Radiodiagnosis; Central nervous system; Encephalon
• ISSN: 0195-6108; 1936-959X
• DOI: 10.3174/ajnr.a2288

Intraoperative brain deformation is an important factor compromising the accuracy of image-guided neurosurgery. The purpose of this study was to elucidate the role of a model-updated image in the compensation of intraoperative brain shift. An FE linear elastic model was built and evaluated in 11 patients with craniotomies. To build this model, we provided a novel model-guided segmentation algorithm. After craniotomy, the sparse intraoperative data (the deformed cortical surface) were tracked by a 3D LRS. The surface deformation, calculated by an extended RPM algorithm, was applied on the FE model as a boundary condition to estimate the entire brain shift. The compensation accuracy of this model was validated by the real-time image data of brain deformation acquired by intraoperative MR imaging. The prediction error of this model ranged from 1.29 to 1.91 mm (mean, 1.62 ± 0.22 mm), and the compensation accuracy ranged from 62.8% to 81.4% (mean, 69.2 ± 5.3%). The compensation accuracy on the displacement of subcortical structures was higher than that of deep structures (71.3 ± 6.1%:66.8 ± 5.0%, P < .01). In addition, the compensation accuracy in the group with a horizontal bone window was higher than that in the group with a nonhorizontal bone window (72.0 ± 5.3%:65.7 ± 2.9%, P < .05). Combined with our novel model-guided segmentation and extended RPM algorithms, this sparse data-driven biomechanical model is expected to be a reliable, efficient, and convenient approach for compensation of intraoperative brain shift in image-guided surgery.