Evaluation of Conoscopic Holography for Estimating Tumor Resection Cavities in Model-Based Image-Guided Neurosurgery

Abstract

Surgical navigation relies on accurately mapping the intraoperative state of the patient to models derived from preoperative images. In image-guided neurosurgery, soft tissue deformations are common and have been shown to compromise the accuracy of guidance systems. In lieu of whole-brain intraoperative imaging, some advocate the use of intraoperatively acquired sparse data from laser-range scans, ultrasound imaging, or stereo reconstruction coupled with a computational model to drive subsurface deformations. Some authors have reported on compensating for brain sag, swelling, retraction, and the application of pharmaceuticals such as mannitol with these models. To date, strategies for modeling tissue resection have been limited. In this paper, we report our experiences with a novel digitization approach, called a conoprobe, to document tissue resection cavities and assess the impact of resection on model-based guidance systems. Specifically, the conoprobe was used to digitize the interior of the resection cavity during eight brain tumor resection surgeries and then compared against model prediction results of tumor locations. We should note that no effort was made to incorporate resection into the model but rather the objective was to determine if measurement was possible to study the impact on modeling tissue resection. In addition, the digitized resection cavity was compared with early postoperative MRI scans to determine whether these scans can further inform tissue resection. The results demonstrate benefit in model correction despite not having resection explicitly modeled. However, results also indicate the challenge that resection provides for model-correction approaches. With respect to the digitization technology, it is clear that the conoprobe provides important real-time data regarding resection and adds another dimension to our noncontact instrumentation framework for soft-tissue deformation compensation in guidance systems.

Android application for determining surgical variables in brain-tumor resection procedures

“…These approaches use data from intraoperative microscopes, 8,9 laser range scanners, 10,11 and conoscopic holography devices 12 in conjunction with advanced modelbased image-to-physical registration approaches to compensate for deformations. Furthermore, while intraoperative imaging modalities, such as iMR and iCT, are quite compelling, workflow friendly, and inexpensive solutions using sparse data are certainly attractive; and there have been a variety of approaches 11,[13][14][15] in this direction.…”

“…1. The activation of this feature, (3), results in the appearance of additional directional features [see slice data controls/craniotomy controls (12)] to move the craniotomy region around the patient's head. In addition, sliders for controlling the size of the craniotomy also appear [see adjust craniotomy shape (9)].…”

“…If the neurosurgeon is unsatisfied with any of the parameters of this region after finalization (size, shape, and location), he or she can use (4), (9), (10), and (12) to plan the craniotomy region again, and perform craniotomy (5) to finalize the new plan (Fig. 2).…”

“…Slice data controls/craniotomy controls If (10) is selected, three sliders will appear in region (12). These sliders allow the neurosurgeon to navigate through the patient's axial, coronal, and sagittal slice image volume.…”

“…These sliders allow the neurosurgeon to navigate through the patient's axial, coronal, and sagittal slice image volume. If (3) is selected, a set of buttons will appear in region (12). These will allow the neurosurgeon to translate and rotate the craniotomy region around the head ( Figs.…”