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

• Published in: IEEE transactions on biomedical engineering Vol. 61; no. 6; pp. 1833 - 1843
• Main Authors: Simpson, Amber L., Sun, Kay, Pheiffer, Thomas S., Rucker, D. Caleb, Sills, Allen K., Thompson, Reid C., Miga, Michael I.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adult; Aged; Aged, 80 and over; Brain; Brain - pathology; Brain - surgery; Brain models; Brain Neoplasms - pathology; Brain Neoplasms - surgery; Brain shift; Cavity resonators; computational modeling; Computer Simulation; conoscopic holography; Deformable models; Deformation; Digitization; evaluation; Female; Guidance systems; Holes; Holography - methods; Humans; Image Processing, Computer-Assisted - methods; image-guided neurosurgery; Imaging; Magnetic Resonance Imaging; Male; Mathematical models; Middle Aged; Neuroimaging - methods; Neurosurgical Procedures - methods; Surgery; Surgery, Computer-Assisted - methods; Tumors; Young Adult
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2014.2308299

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.