Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases

Abstract

Purpose Brain shift during neurosurgical procedures must be corrected for in order to reestablish accurate alignment for successful image-guided tumor resection. Sparse-data-driven biomechanical models that predict physiological brain shift by accounting for typical deformation-inducing events such as cerebrospinal fluid drainage, hyperosmotic drugs, swelling, retraction, resection, and tumor cavity collapse are an inexpensive solution. This study evaluated the robustness and accuracy of a biomechanical model-based brain shift correction system to assist with tumor resection surgery in 16 clinical cases. Methods Preoperative computation involved the generation of a patient-specific finite element model of the brain and creation of an atlas of brain deformation solutions calculated using a distribution of boundary and deformation-inducing forcing conditions (e.g., sag, tissue contraction, and tissue swelling). The optimum brain shift solution was determined using an inverse problem approach which linearly combines solutions from the atlas to match the cortical surface deformation data collected intraoperatively. The computed deformations were then used to update the preoperative images for all 16 patients. Results The mean brain shift measured ranged on average from 2.5 to 21.3 mm, and the biomechanical model-based correction system managed to account for the bulk of the brain shift, producing a mean corrected error ranging on average from 0.7 to 4.0 mm. Conclusions Biomechanical models are an inexpensive means to assist intervention via correction for brain deformations that can compromise surgical navigation systems. To our knowledge, this study represents the most comprehensive clinical evaluation of a deformation correction pipeline for image-guided neurosurgery.

Resection-induced brain-shift compensation using vessel-based methods

“…The order of magnitude of the remaining shit is similar to the ones obtained just after dura opening. 3,5,18 During resection, these results can therefore be considered very satisfying. This can be confirmed qualitatively, as shown in Figures 1 and 2.…”

“…In the literature, craniotomy-induced brain-shift occuring before or just after dura opening is a widely studied topic. [2][3][4][5][6][7] However, fewer works deal with the image-to-patient registration during the whole procedure, to take into account the deformation induced by tissue retraction and tumor resection itself. Some authors have simply used their craniotomy-induced compensation methods, while others have proposed to take account for the cavity formed by the resection.…”

Computational Biomechanics of the Brain in the Operating Theatre

“…When using a linear elastic model, a common approach is to compute and store the deformations resulting from elementary nodal displacements [15]. Other strategies model the non linearity of the brain through pre-computations [16,17,18]. The overall model response to a set of imposed nodal displacements then relies on the principle of superposition, or linear combination of pre-computed deformations.…”

Biomechanical Modeling of Brain Soft Tissues for Medical Applications

“…Laser-range scanners and stereo cameras provide information about the deformations of the exposed cortical surface. The methods relying on such acquisitions (De Lorenzo et al, 2012;Miga et al, 2015) therefore make the strong assumption that all the non-linear 3D deformations of the soft tissues can be extrapolated from the exposed brain surface. However, according to Wittek et al (2007), "the prediction accuracy improves when information about deformation of not only exposed (during craniotomy) but also unexposed parts of the brain surface is used when prescribing loading".…”