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.

Keywords: Biomechanical model; Brain shift; Finite element; Image-guided surgery; Inverse model; Registration.

Fig. 1

A workflow illustrating the preoperative and intraoperative computational processing steps involved in producing an updated brain shift image. The inputs are preoperative MR images, face laser range scan (LRS) for registration, and pre- and postresection cortical brain surface LRS to drive the inverse modeling

Fig. 2

a Preoperative GUI planner with craniotomy location and size tool demonstrated, and b boundary conditions automatically generated from preoperative planning tool with (left) showing fixed brain stem nodes in red, stress-free nodes in green, cranially constrained but lateral freedom in black, with dural septa nodes of falx and tentorium shown in magenta, and tumor shown in blue on the left image. On the (right), drainage conditions are specified with drainage allowed on regions of the brain open to atmosphere in blue with nondraining nodes in red

Fig. 3

For patients #1, 4, 8, 12, and 16, illustrated are a preresection field of view bitmap (FOVBMP), b post-resection FOVBMP, c brain shift as observed by the overlay of deformed (white) and undeformed (red) brain mesh, d top view and e side view of the deformed brain mesh overlaid with the postresection LRS scans, f original MR image, g deformed MR image, and h image difference between original and deformed MR images