106 research outputs found

    Lead-OR: A multimodal platform for deep brain stimulation surgery

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    Background: Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced. Methods: Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform. Results: We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool. Conclusions: This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages

    SEEG assistant: a 3DSlicer extension to support epilepsy surgery

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    Imaging the subthalamic nucleus in Parkinson’s disease

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    This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time

    Optimized Targeting in Deep Brain Stimulation for Movement Disorders.

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    Deep brain stimulation (DBS) is the dominant surgical therapy for medically-refractory Parkinson’s Disease (PD) and Essential Tremor (ET). Despite its success in treating the physical symptoms of many movement disorders, optimal targeting protocols are unknown. The success of the surgery is highly dependent upon proper placement of the electrode in the brain. However, the anatomical targets for PD and ET DBS—the subthalamic nucleus (STN) and ventral intermediate (Vim) nucleus of the thalamus, respectively—are not distinguishable on conventional magnetic resonance imaging. Neurosurgeons typically locate these structures using imprecise atlas-based indirect targeting methods requiring several attempts, increasing the risk of intracranial hemorrhage. The purpose of this work was to optimize targeting in DBS for PD and ET. First, we evaluated the most common indirect STN targeting methods with our validated 3-Tesla MRI protocol optimized for STN visualization. We calculated indirect targets as prescribed by midcommissural point (MCP) -based and red nucleus-based (RN) methods, and compared those coordinates to the position of the STN. We found that RN-based targeting is statistically superior to MCP-based targeting and should be routinely used in the absence of direct STN visualization. In our next study, we investigated the volume of tissue activated (VTA) in thalamic DBS. First, we developed a k-means clustering algorithm that operates on diffusion tensor imaging data to segment the thalamus into its functionally-distinct nuclei. We segmented individual patient thalami and an atlas thalamus in an existing VTA model, and created an individualized VTA model by utilizing each patient’s own anatomy and tissue conductivity. We measured stimulation overlaps with relevant nuclei for clinically efficacious stimulation settings. Our preliminary results indicated that individualized VTA modeling may provide more precise modeling results than existing atlas-based VTA modeling. Next, we investigated the ability of atlas-based and individualized VTA modeling methods to explain common side effects from thalamic DBS. We found that individualized VTA modeling is superior to atlas-based modeling in the prediction of side effects. The results of this work advance the understanding of proper DBS targeting for movement disorders, and our VTA modeling system represents the most individualized approach for ET DBS surgical planning.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111402/1/hlayla_1.pd

    An Active Contour-based Atlas Registration Model for Automatic Subthalamic Nucleus Targeting on MRI: Method and Validation

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    This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert’s variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view
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