CranialVault and its CRAVE tools: A clinical computer assistance system for deep brain stimulation (DBS) therapy

Abstract

A number of methods have been developed to assist surgeons at various stages of deep brain stimulation (DBS) therapy. These include construction of anatomical atlases, functional databases, and electrophysiological atlases and maps. But, a complete system that can be integrated into the clinical workflow has not been developed. In this paper we present a system designed to assist physicians in pre-operative target planning, intra-operative target refinement and implantation, and post-operative DBS lead programming. The purpose of this system is to centralize the data acquired a the various stages of the procedure, reduce the amount of time needed at each stage of the therapy, and maximize the efficiency of the entire process. The system consists of a central repository (CranialVault), of a suite of software modules called CRAVE (CRAnialVault Explorer) that permit data entry and data visualization at each stage of the therapy, and of a series of algorithms that permit the automatic processing of the data. The central repository contains image data for more than 400 patients with the related pre-operative plans and position of the final implants and about 10,550 electrophysiological data points (micro-electrode recordings or responses to stimulations) recorded from 222 of these patients. The system has reached the stage of a clinical prototype that is being evaluated clinically at our institution. A preliminary quantitative validation of the planning component of the system performed on 80 patients who underwent the procedure between January 2009 and December 2009 shows that the system provides both timely and valuable information.

Neuroinformatics in Clinical and Translational Medicine—Novel Approaches

“…This goal requires the removal of individual patient variations (i.e., size of the brain and skull) and reformatting clinical data into a common, uniform pattern, such as an atlas or standard view, so that pathological variations can be recognizable when removed from the individual patient context; 2) The second goal is then to take those patterns, warp them back to an individual patient's morphology or function, and see if abnormalities apply to a specific patient, i.e. to help with diagnosis or etiology of the disease, or to treat a specific disease (D'Hasese et al 2010). For example, this may involve taking a standard brain view or atlas and warping to an individual patient scan to identify a specific region of the brain for treatment targeting.…”

“…However, in many of these databases the data [i.e., brain magnetic resonance imaging (MRI) or computed tomography (CT)] were acquired specifically for particular research purposes, with the requisite cross-site validation and calibration efforts, to enable coherent assembly of the data across a particular class of patients. Another example is a tightly-defined database of neuroimaging and physiological data, designed to be helpful for planning an individual surgical intervention on patients (D'Hasese et al 2010). These well-defined data sets are critical for the development of imaging biomarkers that will aid in assessing treatment response, and making prognostic and diagnostic assessments of future patients.…”

Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization

“…Our dataset contains images of 201 individuals from the data repository we have created over a decade for deep brain stimulation (DBS) surgeries [3]. All these images are T1-weighted sagittal MR image volumes with approximately 256×256×170 1 mm 3 isotropic voxels.…”

Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation

“…The procedure consists in implanting deep brain electrodes with an optimal placement and optimal electrical stimulation parameters that alleviate the symptoms while avoiding structures that trigger side effects. 2 To improve targeting, preoperative patient-specific models are built from multimodal medical images 3,4 such as preoperative magnetic resonance images (MRI) registered with an anatomical [5][6][7] or histological atlas. 8,9 A patient-specific model allows neurosurgeons to visualize on a three-dimensional (3-D) virtual representation of the cerebral structures of interest such as basal ganglia.…”