Comparison of Physics-Based Deformable Registration Methods for Image-Guided Neurosurgery

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration

Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery

During neurosurgery, medical images of the brain are used to locate tumors and critical structures, but brain tissue shifts make pre-operative images unreliable for accurate removal of tumors. Intra-operative imaging can track these deformations but is not a substitute for pre-operative data. To address this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and time-consuming image processing operation that adjusts the pre-operative image data to account for intra-operative brain shift. Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method. We demonstrate that our implementation enables NRR results to be delivered within clinical time constraints while leveraging Distributed Computing and Machine Learning to enhance registration accuracy by identifying optimal parameters for the NRR method. Additionally, we highlight challenges associated with its use in the operating room

SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research

International audienceDiffusion magnetic resonance imaging (dMRI) is the only non-invasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomical assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer (www.slicer.org). More information, videos, tutorials, and sample data are available at dmri.slicer.org

AES 2011 Abstract Title: Temporal theta oscillation enhancement predicts successful memory encoding

Abstract Rationale Theta oscillations The amplitude of these oscillations was correlated to successful memory retrieval in a verbal memory task Methods Epileptic patients implanted with subdural electrodes for seizure localization were tested on two tasks. The first one was a classic multi-item short-term memory task The subjects had to indicate using a key press whether the test image was part of the previous image series or not. In the second task, the subjects were shown a series of 4-6 images. After a short delay, the patients were instructed to arrange the previously presented objects in the order in which they appeare

AES 2011 Abstract Title: Temporal theta oscillation enhancement predicts successful memory encoding

Abstract Rationale Theta oscillations The amplitude of these oscillations was correlated to successful memory retrieval in a verbal memory task Methods Epileptic patients implanted with subdural electrodes for seizure localization were tested on two tasks. The first one was a classic multi-item short-term memory task The subjects had to indicate using a key press whether the test image was part of the previous image series or not. In the second task, the subjects were shown a series of 4-6 images. After a short delay, the patients were instructed to arrange the previously presented objects in the order in which they appeare