Pierre-François D'Haese

Deep brain stimulation in early stage Parkinson's disease: operative experience from a prospective randomised clinical trial

Background Recent evidence suggests that deep brain stimulation of the subthalamic nucleus (STN-DBS) may have a disease modifying effect in early Parkinsons disease (PD). A randomised, prospective study is underway to determine whether STN-DBS in early PD is safe and tolerable. Objectives/methods 15 of 30 early PD patients were randomised to receive STN-DBS implants in an institutional review board approved protocol. Operative technique, location of DBS leads and perioperative adverse events are reported. Active contact used for stimulation in these patients was compared with 47 advanced PD patients undergoing an identical procedure by the same surgeon. Results 14 of the 15 patients did not sustain any long term (>3 months) complications from the surgery. One subject suffered a stroke resulting in mild cognitive changes and slight right arm and face weakness. The average optimal contact used in symptomatic treatment of early PD patients was: anterior −1.1±1.7 mm, lateral 10.7±1.7 mm and superior −3.3±2.5 mm (anterior and posterior commissure coordinates). This location is statistically no different (0.77 mm, p>0.05) than the optimal contact used in the treatment of 47 advanced PD patients. Conclusions The perioperative adverse events in this trial of subjects with early stage PD are comparable with those reported for STN-DBS in advanced PD. The active contact position used in early PD is not significantly different from that used in late stage disease. This is the first report of the operative experience from a randomised, surgical versus best medical therapy trial for the early treatment of PD.

Customized, miniature rapid-prototype stereotactic frames for use in deep brain stimulator surgery: initial clinical methodology and experience from 263 patients from 2002 to 2008.

Background: The microTargeting™ platform (MTP) stereotaxy system (FHC Inc., Bowdoin, Me., USA) was FDA approved in 2001 utilizing rapid-prototyping technology to create custom platforms for human stereotaxy procedures. It has also been called the STarFix (surgical targeting fixture) system since it is based on the concept of a patient- and procedure-specific surgical fixture. This is an alternative stereotactic method by which planned trajectories are incorporated into custom-built, miniature stereotactic platforms mounted onto bone fiducial markers. Our goal is to report the clinical experience with this system over a 6-year period. Methods: We present the largest reported series of patients who underwent deep brain stimulation (DBS) implantations using customized rapidly prototyped stereotactic frames (MTP). Clinical experience and technical features for the use of this stereotactic system are described. Final lead location analysis using postoperative CT was performed to measure the clinical accuracy of the stereotactic system. Results: Our series included 263 patients who underwent 284 DBS implantation surgeries at one institution over a 6-year period. The clinical targeting error without accounting for brain shift in this series was found to be 1.99 mm (SD 0.9). Operating room time was reduced through earlier incision time by 2 h per case. Conclusion: Customized, miniature stereotactic frames, namely STarFix platforms, are an acceptable and efficient alternative method for DBS implantation. Its clinical accuracy and outcome are comparable to those associated with traditional stereotactic frame systems.

Automatic Segmentation of Brain Structures for Radiation Therapy Planning

Delineation of structures to irradiate (the tumors) as well as structures to be spared (e.g., optic nerve, brainstem, or eyes) is required for advanced radiotherapy techniques. Due to a lack of time and the number of patients to be treated these cannot always be segmented accurately which may lead to suboptimal plans. A possible solution is to develop methods to identify these structures automatically. This study tests the hypothesis that a fully automatic, atlas-based segmentation method can be used to segment most brain structures needed for radiotherapy plans even tough tumors may deform normal anatomy substantially. This is accomplished by registering an atlas with a subject volume using a combination of rigid and non-rigid registration algorithms. Segmented structures in the atlas volume are then mapped to the corresponding structures in the subject volume using the computed transformations. The method we propose has been tested on two sets of data, i.e., adults and children/young adults. For the first set of data, contours obtained automatically have been compared to contours delineated manually by three physicians. For the other set qualitative results are presented.

Non-rigid registration algorithm with spatially varying stiffness properties

Non-rigid registration algorithms have been proposed over the years to register medical images to each other. One class of applications for these algorithms is the automatic segmentation of structures and substructures using a predefined atlas. But these algorithms have been limited to image volumes without gross abnormalities or pathologies and have thus been of limited use for applications such as the automatic segmentation of radiation sensitive structures for radiation therapy planning. The algorithm we present in this paper is an extension of a non-rigid registration algorithm we have previously developed (the adaptive basis algorithm). This extension permits the use of the algorithm for the automatic segmentation of medical images even when structures have been displaced substantially. The algorithm automatically adjusts the stiffness of the transformation to permit larger displacements over regions that are known to be very compliant and smaller displacements over regions that are known to be less compliant. The stiffness map can be defined once and for all in an atlas. The algorithm has been tested and evaluated on head image volumes with large ventricular enlargements and head image volumes with large space-occupying lesions.

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.

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.

Atlas-based segmentation of the brain for 3-dimensional treatment planning in children with infratentorial ependymoma

This paper presents a fully automated brain segmentation method that has been applied to a group of patients with infratentorial ependymoma. The purpose of the study was to test the hypothesis that fully-automated atlas-based segmentation methods provide useful normal tissue dosimetry from which dose-volume modeling may be performed in a manner equivalent to dose-volume data obtained from manual contouring. To test this hypothesis, we compared the integrated average dose for three small (chiasm, pituitary, hypothalamus) and three large (temporal lobes and total brain) normal tissue structures from ten patients using automated and manual contouring. There was no significant difference in the calculated average dose for the structures of interest. The greatest difference was noted for smaller structures which were located along the midline and in the gradient of dose. The results of this study form the basis of an ongoing larger study involving similar patients to evaluate automated and manual contouring as well as the clinical significance of any differences using dose-volume modeling.

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.

Validation of a Nonrigid Registration Error Detection Algorithm Using Clinical MRI Brain Data

Identification of error in nonrigid registration is a critical problem in the medical image processing community. We recently proposed an algorithm that we call “Assessing Quality Using Image Registration Circuits” (AQUIRC) to identify nonrigid registration errors and have tested its performance using simulated cases. In this paper, we extend our previous work to assess AQUIRCs ability to detect local nonrigid registration errors and validate it quantitatively at specific clinical landmarks, namely the anterior commissure and the posterior commissure. To test our approach on a representative range of error we utilize five different registration methods and use 100 target images and nine atlas images. Our results show that AQUIRCs measure of registration quality correlates with the true target registration error (TRE) at these selected landmarks with an R2=0.542. To compare our method to a more conventional approach, we compute local normalized correlation coefficient (LNCC) and show that AQUIRC performs similarly. However, a multi-linear regression performed with both AQUIRCs measure and LNCC shows a higher correlation with TRE than correlations obtained with either measure alone, thus showing the complementarity of these quality measures. We conclude the paper by showing that the AQUIRC algorithm can be used to reduce registration errors for all five algorithms.