Srivatsan Pallavaram

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

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 CRAnialVault Explorer (CRAVE) 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.

Clinical Accuracy of a Customized Stereotactic Platform for Deep Brain Stimulation after Accounting for Brain Shift

Previous studies have evaluated the accuracy of several approaches for the placement of electrodes for deep brain stimulation. In this paper, we present a strategy to minimize the effect of brain shift on the estimation of the electrode placement error (EPE) for a stereotactic platform in the absence of intraoperative imaging data, and we apply it to the StarFix microTargeting® Platform (FHC Inc., Bowdoin, Me., USA). This method involves comparing the intraoperative stereotactic coordinates of the implant with its position in the postoperative CT images in a population for which the effect of brain shift is minimal. The study we have conducted on 75 patients demonstrates that the EPE is overestimated at least by about 60% if brain shift is not taken into account, and shows a clinical accuracy of 1.24 ± 0.37 mm for the StarFix frame, which is similar to the reported G frame accuracy and better than the reported Nexframe accuracy (2.5 ± 1.4 mm) [Stereotact Funct Neurosurg 2007;85:235–242].

Intersurgeon Variability in the Selection of Anterior and Posterior Commissures and Its Potential Effects on Target Localization

Background: This study reports the intersurgeon variability in manual selection of the anterior and posterior commissures (AC and PC). The study also investigates the effect of this variability on the localization of targets like the subthalamic nucleus, ventralis intermedius nucleus and globus pallidus internus. The additional effect of variation in the selection of the mid-plane on target localization is also evaluated. Methods: 43 neurosurgeons (38 attendings, 5 residents/ fellows) were asked to select the AC and the PC points (as routinely used for stereotactic neurosurgical planning) on two MRI scans. The corresponding mid-commissural points (MCPs) and target coordinates were calculated. Results: The collected data show that the MCP is more reliable than either the AC or the PC points. These data also show that, even for experienced neurosurgeons, variations in selecting the AC and the PC point result in substantial variations at the target points: 1.15 ± 0.89 mm, 1.45 ± 1.25 mm, 1.21 ± 0.83 for the subthalamic nucleus, ventralis intermedius nucleus, and globus pallidus internus, respectively, for the first MRI volumeand 1.08 ± 1.37 mm, 1.35 ± 1.71 mm, 1.12 ± 1.17 mm for the same structures for the second volume. These variations are larger when residents/fellows are included in the data set. Conclusions: The data collected in this study highlight the difficulty in establishing a common reference system that can be used to communicate target location across sites. It indicates the need for the development and evaluation of alternative normalization methods that would permit specifying targets directly in image coordinates or the development of improved imaging techniques that would permit direct targeting.

Automatic selection of DBS target points using multiple electrophysiological atlases

In this paper we study and evaluate the influence of the choice of a particular reference volume as the electrophysiological atlas on the accuracy of the automatic predictions of optimal points for deep brain stimulator (DBS) implants. We refer to an electrophysiological atlas as a spatial map of electrophysiological information such as micro electrode recordings (MER), stimulation parameters, final implants positions, etc., which are acquired for each patient and then mapped onto a single reference volume using registration algorithms. An atlas-based prediction of the optimal point for a DBS surgery is made by registering a patients image volume to that reference volume, that is, by computing a correct coordinate mapping between the two; and then by projecting the optimal point from the atlas to the patient using the transformation from the registration algorithm. Different atlases, as well as different parameterizations of the registration algorithm, lead to different and somewhat independent atlas-based predictions. We show how the use of multiple reference volumes can improve the accuracy of prediction by combining the predictions from the multiple reference volumes weighted by the accuracy of the non-rigid registration between each of the corresponding atlases and the patient volume.

A New Method for Creating Electrophysiological Maps for DBS Surgery and Their Application to Surgical Guidance

Electrophysiological maps based on a Gaussian kernel have been proposed as a means to visualize response to stimulation in deep brain stimulation (DBS) surgeries. However, the Gaussian model does not represent the underlying physiological phenomenon produced by stimulation. We propose a new method to create physiological maps, which relies on spherical shell kernels. We compare our new maps to those created with Gaussian kernels and show that, on simulated data, this new approach produces more realistic maps. Experiments we have performed with real patient data show that our new maps correlate well with the underlying anatomy. Finally, we present preliminary results on an ongoing study assessing the value of these maps as pre-operative planning and intra-operative guidance tools.

Validation of a Fully Automatic Method for the Routine Selection of the Anterior and Posterior Commissures in Magnetic Resonance Images

The anterior and posterior commissures (AC and PC) typically form the reference points of the stereotactic coordinate system. Hence any discussion of target localization is limited by the variability of AC and PC selection. In an earlier study, which was performed using manual selections of AC and PC by 43 neurosurgeons, we showed that intersurgeon variability has a substantial impact on the localization of deep brain stimulation targets. We have developed and validated a fully automatic and robust AC and PC selection system that can be routinely used clinically. In this study, we show that this system is capable of localizing the AC and PC points with an accuracy that is better than that achieved clinically by manual selection, 0.65 mm (95% confidence interval: 0.56–0.79) versus 1.21 mm (95% confidence interval: 0.91–1.47) for AC and 0.56 mm (95% confidence interval: 0.46–0.66) versus 1.06 mm (95% confidence interval: 0.82–1.26) for PC.

A Surgeon Specific Automatic Path Planning Algorithm for Deep Brain Stimulation

In deep brain stimulation surgeries, stimulating electrodes are placed at specific targets in the deep brain to treat neurological disorders. Reaching these targets safely requires avoiding critical structures in the brain. Meticulous planning is required to find a safe path from the cortical surface to the intended target. Choosing a trajectory automatically is difficult because there is little consensus among neurosurgeons on what is optimal. Our goals are to design a path planning system that is able to learn the preferences of individual surgeons and, eventually, to standardize the surgical approach using this learned information. In this work, we take the first step towards these goals, which is to develop a trajectory planning approach that is able to effectively mimic individual surgeons and is designed such that parameters, which potentially can be automatically learned, are used to describe an individual surgeons preferences. To validate the approach, two neurosurgeons were asked to choose between their manual and a computed trajectory, blinded to their identity. The results of this experiment showed that the neurosurgeons preferred the computed trajectory over their own in 10 out of 40 cases. The computed trajectory was judged to be equivalent to the manual one or otherwise acceptable in 27 of the remaining cases. These results demonstrate the potential clinical utility of computer-assisted path planning.

Fully automated targeting using nonrigid image registration matches accuracy and exceeds precision of best manual approaches to subthalamic deep brain stimulation targeting in Parkinson disease.

BACKGROUND Finding the optimal location for the implantation of the electrode in deep brain stimulation (DBS) surgery is crucial for maximizing the therapeutic benefit to the patient. Such targeting is challenging for several reasons, including anatomic variability between patients as well as the lack of consensus about the location of the optimal target. OBJECTIVE To compare the performance of popular manual targeting methods against a fully automatic nonrigid image registration-based approach. METHODS In 71 Parkinson disease subthalamic nucleus (STN)-DBS implantations, an experienced functional neurosurgeon selected the target manually using 3 different approaches: indirect targeting using standard stereotactic coordinates, direct targeting based on the patient magnetic resonance imaging, and indirect targeting relative to the red nucleus. Targets were also automatically predicted by using a leave-one-out approach to populate the CranialVault atlas with the use of nonrigid image registration. The different targeting methods were compared against the location of the final active contact, determined through iterative clinical programming in each individual patient. RESULTS Targeting by using standard stereotactic coordinates corresponding to the center of the motor territory of the STN had the largest targeting error (3.69 mm), followed by direct targeting (3.44 mm), average stereotactic coordinates of active contacts from this study (3.02 mm), red nucleus-based targeting (2.75 mm), and nonrigid image registration-based automatic predictions using the CranialVault atlas (2.70 mm). The CranialVault atlas method had statistically smaller variance than all manual approaches. CONCLUSION Fully automatic targeting based on nonrigid image registration with the use of the CranialVault atlas is as accurate and more precise than popular manual methods for STN-DBS.

Multisurgeon, multisite validation of a trajectory planning algorithm for deep brain stimulation procedures.

Deep brain stimulation, which is used to treat various neurological disorders, involves implanting a permanent electrode into precise targets deep in the brain. Reaching these targets safely is difficult because surgeons have to plan trajectories that avoid critical structures and reach targets within specific angles. A number of systems have been proposed to assist surgeons in this task. These typically involve formulating constraints as cost terms, weighting them by surgical importance, and searching for optimal trajectories, in which constraints and their weights reflect local practice. Assessing the performance of such systems is challenging because of the lack of ground truth and clear consensus on an optimal approach among surgeons. Due to difficulties in coordinating inter-institution evaluation studies, these have been performed so far at the sites at which the systems are developed. Whether or not a scheme developed at one site can also be used at another is thus unknown. In this paper, we conduct a study that involves four surgeons at three institutions to determine whether or not constraints and their associated weights can be used across institutions. Through a series of experiments, we show that a single set of weights performs well for all surgeons in our group. Out of 60 trajectories, our trajectories were accepted by a majority of neurosurgeons in 95% of the cases and the average acceptance rate was 90%. This study suggests, albeit on a limited number of surgeons, that the same system can be used to provide assistance across multiple sites and surgeons.

The VU-DBS project: integrated and computer-assisted planning, intra-operative placement, and post-operative programming of deep-brain stimulators.

Movement disorders affect over 5,000,000 people in the United States. Contemporary treatment of these diseases involves high-frequency stimulation through deep brain stimulation (DBS). This form of therapy is offered to patients who have begun to see failure with standard medical therapy and also to patients for which medical therapy is poorly effective. A DBS procedure involves the surgical placement, with millimetric accuracy, of an electrode in the proximity of functional areas referred to as targets. Following the surgical procedure, the implant, which is a multi-contact electrode is programmed to alleviate symptoms while minimizing side effects. Surgical placement of the electrode is difficult because targets of interest are poorly visible in current imaging modalities. Consequently, the process of implantation of a DBS electrode is an iterative procedure. An approximate target position is determined pre-operatively from the position of adjacent structures that are visible in MR images. With the patient awake, this position is then adjusted intra-operatively, which is a lengthy process. The post-surgical programming of the stimulator is an equally challenging and time consuming task, with parameter setting combinations exceeding 4000. This paper reports on the status of the Vanderbilt University DBS Project, which involves the development and clinical evaluation of a system designed to facilitate the entire process from the time of planning to the time of programming.