Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified. The presented method aims to enhance DSA image series by highlighting critical information via automatic classification of vessels using a combination of two learning models: An unsupervised machine learning method based on Independent Component Analysis that decomposes the phases of flow and a convolutional neural network that automatically delineates the vessels in image space. The proposed method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins that provides a viable solution to enhance visualizations for clinical use.
Objective: This purpose of this study was to develop and validate methods for monitoring the progression of resection during the neurosurgical removal of brain tumors. Methods: An optical tracking array was attached to a surgical instrument to track its tip position relative to preoperative imaging. The tip position was monitored continuously during surgery and used to generate a map of resection progression in the form of a 3D distance field. This method was validated on 18 brain phantoms by comparing resection maps to 3D ultrasound acquired before and after resection. A clinically compatible workflow was iteratively developed and optimized during more than 15 clinical cases with input and feedback from two neurosurgeons. Results: Phantom studies showed that resection maps accurately model the actual resection cavity, with an average of 97.5% of voxels inside the actual resection cavity lying within 2 mm of the resection map and an average of 94.7% of tracked resection points lying within 2 mm of the actual resection cavity. In clinical workflow studies, a variety of surgical instruments were tracked continuously with minimal disruption to surgery. Preliminary clinical data demonstrated that the resection map can be used to flag brain shift and predict residual tumor. Conclusion: Continuous monitoring of the progression of brain tumor resection can be done accurately and with minimal disruption in the operating room. Significance: Providing a real-time map of resection progression will reduce the cognitive load on neurosurgeons and facilitate more complete resections, particularly during long, complex surgeries.
Brain shift is a non-rigid deformation of brain tissue that is affected by loss of cerebrospinal fluid, tissue manipulation and gravity among other phenomena. This deformation can negatively influence the outcome of a surgical procedure since surgical planning based on pre-operative image becomes less valid. We present a novel method to compensate for brain shift that maps preoperative image data to the deformed brain during intra-operative neurosurgical procedures and thus increases the likelihood of achieving a gross total resection while decreasing the risk to healthy tissue surrounding the tumor. Through a 3D/2D non-rigid registration process, a 3D articulated model derived from pre-operative imaging is aligned onto 2D images of the vessels viewed through the surgical miscroscopic intra-operatively. The articulated 3D vessels constrain a volumetric biomechanical model of the brain to propagates cortical vessel deformation to the parenchyma and in turn to the tumor. The 3D/2D non-rigid registration is performed using an energy minimization approach that satisfies both projective and physical constraints. Our method is evaluated on real and synthetic data of human brain showing both quantitative and qualitative results and exhibiting its particular suitability for real-time surgical guidance.
KEYWORDS: Brain, Finite element methods, 3D modeling, Tumors, Surgery, Neuroimaging, 3D image processing, Magnetic resonance imaging, Data modeling, Ultrasonography
Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.
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