No abstract

No abstract

BACKGROUND Virtual reality (VR) offers an interactive environment for visualizing the intimate three-dimensional (3D) relationship between a patient’s pathology and surrounding anatomy. The authors present a model for using personalized VR technology, applied across the neurosurgical treatment continuum from the initial consultation to preoperative surgical planning, then to intraoperative navigation, and finally to postoperative visits, for various tumor and vascular pathologies. OBSERVATIONS Five adult patients undergoing procedures for spinal cord cavernoma, clinoidal meningioma, anaplastic oligodendroglioma, giant aneurysm, and arteriovenous malformation were included. For each case, 360-degree VR (360°VR) environments developed using Surgical Theater were used for patient consultation, preoperative planning, and/or intraoperative 3D navigation. The custom 360°VR model was rendered from the patient’s preoperative imaging. For two cases, the plan changed after reviewing the patient’s 360°VR model from one based on conventional Digital Imaging and Communications in Medicine imaging. LESSONS Live 360° visualization with Surgical Theater in conjunction with surgical navigation helped validate the decisions made intraoperatively. The 360°VR models provided visualization to better understand the lesion’s 3D anatomy, as well as to plan and execute the safest patient-specific approach, rather than a less detailed, more standardized one. In all cases, preoperative planning using the patient’s 360°VR model had a significant impact on the surgical approach.

No abstract

Background Diffusion tensor imaging (DTI) is a commonly utilized pre-surgical tractography technique. Despite widespread use, DTI suffers from several critical limitations. These include an inability to replicate crossing fibers and a low angular-resolution, affecting quality of results. More advanced, non-tensor methods have been devised to address DTIs shortcomings, but they remain clinically underutilized due to lack of awareness, logistical and cost factors. Objective Nath et al. (2020) described a method of transforming DTI data into non-tensor high-resolution data, suitable for tractography, using a deep learning technique. This study aims to apply this technique to real-life tumor cases. Methods The deep learning model utilizes a residual convolutional neural network architecture to yield a spherical harmonic representation of the diffusion-weighted MR signal. The model was trained using normal subject data. DTI data from clinical cases were utilized for testing: Subject 1 had a right-sided anaplastic oligodendroglioma. Subject 2 had a right-sided glioblastoma. We conducted deterministic fiber tractography on both the DTI data and the post-processed deep learning algorithm datasets. Results Generally, all tracts generated using the deep learning algorithm dataset were qualitatively and quantitatively (in terms of tract volume) superior than those created with DTI data. This was true for both test cases. Conclusions We successfully utilized a deep learning technique to convert standard DTI data into data capable of high-angular resolution tractography. This method dispenses with specialized hardware or dedicated acquisition protocols. It presents an economical and logistically feasible method for increasing access to high definition tractography imaging clinically.