Near Real-Time Computer Assisted Surgery for Brain Shift Correction Using Biomechanical Models

Abstract

Conventional image-guided neurosurgery relies on preoperative images to provide surgical navigational information and visualization. However, these images are no longer accurate once the skull has been opened and brain shift occurs. To account for changes in the shape of the brain caused by mechanical (e.g., gravity-induced deformations) and physiological effects (e.g., hyperosmotic drug-induced shrinking, or edema-induced swelling), updated images of the brain must be provided to the neuronavigation system in a timely manner for practical use in the operating room. In this paper, a novel preoperative and intraoperative computational processing pipeline for near real-time brain shift correction in the operating room was developed to automate and simplify the processing steps. Preoperatively, a computer model of the patient’s brain with a subsequent atlas of potential deformations due to surgery is generated from diagnostic image volumes. In the case of interim gross changes between diagnosis, and surgery when reimaging is necessary, our preoperative pipeline can be generated within one day of surgery. Intraoperatively, sparse data measuring the cortical brain surface is collected using an optically tracked portable laser range scanner. These data are then used to guide an inverse modeling framework whereby full volumetric brain deformations are reconstructed from precomputed atlas solutions to rapidly match intraoperative cortical surface shift measurements. Once complete, the volumetric displacement field is used to update, i.e., deform, preoperative brain images to their intraoperative shifted state. In this paper, five surgical cases were analyzed with respect to the computational pipeline and workflow timing. With respect to postcortical surface data acquisition, the approximate execution time was 4.5 min. The total update process which included positioning the scanner, data acquisition, inverse model processing, and image deforming was ∼ 11–13 min. In addition, easily implemented hardware, software, and workflow processes were identified for improved performance in the near future.

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

“…An alternative strategy is to build a computational model (e.g., a Finite Element Model) of the brain based on constitutive constraints, which describe the stress-strain response of the tissue under various loading conditions. This model is combined with sparse intraoperative image data to update preoperative images [45, 53–55, 57, 58, 71–76, 81–86]. A summary of the compensation techniques for brain shift is shown in Table 3.…”

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

“…The rate limiting step in providing updated guidance at other time points primarily revolves around the acquisition of additional cortical surface data and determining the cortical surface displacements to drive the inverse model. While the use of the LRS to achieve this end has been well documented in the literature [24,25], other groups have focused on the use of stereo-pair reconstruction to acquire textured cortical surface digitizations [20]. Similarly, Kumar et al [26] presented efforts toward the realization of a robust, real-time stereo-vision system using the surgical microscope with the novel addition of an accuracy comparison between stereo-reconstruction and LRS-generated results.…”

“…More recently, work by Simpson et al [27] summarizes clinical experience with conoscopic holography for digitizing tumor resection cavities. The tumor resection cavity digitizations were then used to validate the deformation correction pipeline presented previously [17,20]. While these studies shed light on performance, an extensive iMR validation of this candidate pipeline is clearly needed, and work towards this is underway.…”

“…Each material can be prescribed different mechanical and hydraulic properties. With respect to material property values, the work in [20] was followed.…”

“…In the Correction GUI [20], these two LRS scans (before and after resection) are used to provide sparse measurements of brain shift. More specifically, the 2D pre- and postresection bitmaps associated with LRS data are used for the selection of homologous points.…”