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.

Fig. 1

Conoprobe surface acquisition of patient enrolled in our study undergoing resection surgery at Vanderbilt University Medical Center (Nashville, TN, USA). The single laser point is visible on the surface of the brain and the optical tracking target can be seen on the conoprobe.

Fig. 2

Conoprobe points (green) and tumor (gray) tumor overlaid on the image volume in three views for two representative cases. Patient 1 is illustrated without correction (a) and with correction (b) representing a directional error due to a lack of corresponding points. Patient 2 is illustrated with without correction (c) and with correction (d) representing less than ideal correction results.

Fig. 3

Conoprobe points (green) and tumor (gray) tumor overlaid on the image volume in three views for one representative case. Results for patient 6 are illustrated without correction (a) and with favorable correction (b).

Fig. 4

Rendering of the conoprobe points (green), tumor (blue), and brain surface (gray) for patient 5 before (a) and after (b) correction. The conoprobe points lie well below the tumor, likely due to cavity collapse during surgery. For comparison, correction using homologous points from postoperative MRI scans described in Section III-B2. (a) Patient 5: No correction. (b) Patient 5: With correction. (c) Patient 5: With correction.

Fig. 5

Preoperative MRI overlaid on the postoperative MRI for patient 3. The outline of the large tumor is visible in the preoperative scan as well as the resected tumor bed (red mass) in the postoperative scan. Significant shift occurred in this case due to the size and position of the tumor. The postoperative MRI demonstrates collapse of the tumor making the scan unusable for retrospective evaluation.

Fig. 6

Conoprobe points (green) rendered with the postoperative MRI scan for patient 5. The conoprobe points lie on the extents of the resected tissue.

Fig. 7

Deformation correction framework developed by our research group.