16 research outputs found
Functional and structural methods for minimally invasive treatment of epilepsy
No full textMedical data analysis for minimally invasive treatment of epileps
New approximation of a scale space kernel on SE(3) and applications in neuroimaging
No full textWe provide a new, analytic kernel for scale space filtering of dMRI data. The kernel is an approximation for the Green’s function of a hypo-elliptic diffusion on the 3D rigid body motion group SE(3), for fiber enhancement in dMRI. The enhancements are described by linear scale space PDEs in the coupled space of positions and orientations embedded in SE(3). As initial condition for the evolution we use either a Fiber Orientation Distribution (FOD) or an Orientation Density Function (ODF). Explicit formulas for the exact kernel do not exist. Although approximations well-suited for fast implementation have been proposed in literature, they lack important symmetries of the exact kernel. We introduce techniques to include these symmetries in approximations based on the logarithm on SE(3), resulting in an improved kernel. Regarding neuroimaging applications, we apply our enhancement kernel (a) to improve dMRI tractography results and (b) to quantify coherence of obtained streamline bundles.
Keywords: Scale space on SE(3); Contextual enhancement; Left-invariant diffusion; Group convolution; Tractograph
Modeling of intracerebral interictal epileptic discharges : evidence for network interactions
No full textPurpose: Stereotactic EEG (SEEG) recordings are considered to be the best choice for preoperative invasive evaluation when the epilepsy of the patient is suspected to originate in deep-sited anatomical structures and standard electro-clinical examinations are not conclusive. The interictal epileptic discharges (IEDs) occurring in these recordings in general are abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. Method: A framework is developed to model the spatiotemporal net-work interactions underlying the IEDs. To identify the highly synchronized neural activity underlying these discharges, the variation in correlation over time of the SEEG signals is related to the occurrence of the IEDs using the general linear model [van Houdt et al., 2012]. Subsequently, it is assessed whether the brain regions that reflect highly synchronized neural activity are either independent or interacting within an epileptic network. Independent component analysis is applied followed by clustering of the spatial distributions of the independent components. The spatial distributions of the spike clusters are visualized together with the estimated time delays against the patients’ brain anatomy [Meesters et al., 2015]. Results: The analysis framework was evaluated for five patients who underwent SEEG recordings prior to successful epilepsy surgery. The spatial distribution of the spike cluster that was related to the MRI-visible brain lesions coincided with the seizure onset zone of these patients. Unraveling of the complex network interactions underlying the IEDs of two more patients without satisfactory surgical outcome indicated that an alternative and plausible resection strategy could have been considered. Conclusion: The analysis framework applied to IEDs is considered a valuable additional tool to the current seizure assessment approach, which might lead to a more successful outcome of epilepsy surgery. Acknowledgement: This study is part of the DeNeCor-project that has received funding from the ENIAC Joint Undertaking (grant no. 324257)
Automated tractography of four white matter fascicles in support of brain tumor surgery
No full textKnowledge of eloquent white matter fascicles is imperative to prevent the loss of sensory processing, linguistic ability and motor skills.\u3cbr/\u3e • Diffusion-weighted tractography methods have made it possible to accurately reconstruct these white matter structures in-vivo [1].\u3cbr/\u3e • However, few neurosurgeons have access to this information because data analysis requires skilled and experienced personnel [2].\u3cbr/\u3e • An automated turn-key tractography pipeline is introduced for four eloquent fascicles and evaluated for brain tumor patients.\u3cbr/\u3
The influence of construction methodology on structural brain network measures:a review
No full textStructural brain networks based on diffusion MRI and tractography show robust attributes such as small-worldness, hierarchical modularity, and rich-club organization. However, there are large discrepancies in the reports about specific network measures. It is hypothesized that these discrepancies result from the influence of construction methodology. We surveyed the methodological options and their influences on network measures. It is found that most network measures are sensitive to the scale of brain parcellation, MRI gradient schemes and orientation model, and the tractography algorithm, which is in accordance with the theoretical analysis of the small-world network model. Different network weighting schemes represent different attributes of brain networks, which makes these schemes incomparable between studies. Methodology choice depends on the specific study objectives and a clear understanding of the pros and cons of a particular methodology. Because there is no way to eliminate these influences, it seems more practical to quantify them, optimize the methodologies, and construct structural brain networks with multiple spatial resolutions, multiple edge densities, and multiple weighting schemes
Automated 2D ultrasound fusion imaging of abdominal aortic aneurysms
No full text\u3cp\u3eRecent studies reveal the benefits of ultrasound strain imaging and elastography of abdominal aortic aneurysms (AAAs). However, feasibility and reproducibility of these techniques is not trivial due to the low imaging depth and low contrast of the US data. Beam-steering overcomes these problems in superficial arteries, but is not applicable for AAAs. Multi-angle acquisition could improve both aortic wall segmentation and strain imaging in a similar fashion. In this study, an automated technique for fusion of two-dimensional images, acquired manually at different positions, was developed and applied to ultrasound data of AAAs (n = 5). It was attempted to acquire images at-45, 0 and 45 degrees. Feature points were detected using a scale-space approach and were clustered based on anisotropy of the neighborhood. Next, an ellipsoid was fit through the remaining points. By registering these ellipses, the three different images were compounded. Initial results reveal that the method is able to perform automated registration. The estimated angle between the left and middle images was-25°+/-16°and was 37°+/-15°between the middle and right position (n = 4). The ellipsoid fit showed more variation in lateral direction. However, additional features should be considered for registration. Results suggest that automated wall thickness assessment might be possible using the extracted feature points.\u3c/p\u3
Multi atlas-based muscle segmentation in abdominal CT images with varying field of view
No full textThe development of automatic techniques for the analysis of abdominal CT images is a topic of large
interest. By using automatic techniques, objective diagnostic support can be provided to physicians and organ segmentation can eliminate time-consuming manual procedures such as delineation. Automatic kidney segmentation has been achieved for healthy cases but is unsuccessful in cases with diseased kidney. In this paper we propose an automatic system to assist the segmentation of abdominal organs, using the medially positioned psoas major muscles' shape and location along with previously accomplished segmentations of the liver and spleen. A framework is employed to segment the vertebral column and ribbones, and the left and right psoas major muscles are segmented using a multi-atlas-based segmentation with weighted decision fusion and non-rigid registration. Due to a varying field of view (FOV) in each dataset and the requirement of an equal FOV for registration, an adjustment is made between pairs of datasets using an automatic vertebra identification framework created in this paper. The vertebra identification shows desired results in 88% of 68 datasets. The psoas major segmentation accuracy is inspected using a cross-validation among 21 datasets, showing a median Jaccard similarity coefficient (JSC) of 63.4% and 68.6% for the left and right muscles respectively. Future work will focus on adapting the kidney segmentation framework to include the shape and position of the psoas major muscle in the processing
Optimal paths for variants of the 2D and 3D reeds-shepp car with applications in image analysis
No full textWe introduce a PDE-based approach for finding minimal paths for the Reeds-Shepp car. In our model we minimize a (data-driven) functional involving both curvature and length penalization, with several generalizations. Our approach encompasses the 2D and 3D variants of this model, state dependent costs, and the possibility of removing the reverse gear of the vehicle. The model without reverse gear resembles Dubins's car, but without imposing a constraint on the curvature. We solve our model via eikonal equations on the manifold R^d ×S^{d−1} with respect to highly anisotropic Finsler functions, which approximate the singular (pseudo)-metrics underlying the model. This is achieved using a Fast-Marching method, based on specific discretization stencils which are adapted to the preferred directions of the metric and obey a generalized acuteness property. We justify our approach by convergence results. Our curve optimization model in R^d × S^{d−1} with data-driven cost allows to extract complex tubular structures from medical images and incomplete data due to occlusions or low contrast. Our work extends the results of Sanguinetti et al. on numerical sub-Riemannian eikonal equations and the Reeds-Shepp Car: we consider a 3D extension, and a new model without reverse gear which better handles bifurcations, relying on previous work by Mirebeau for the numerics. The anisotropic fast-marching approach is optimal for efficiency with limited loss of accuracy, although the differences compared to exact solutions by Duits et al. do become noticeable. Numerical experiments show the high potential of our method in two applications: retinal vessel tree extraction in 2D fundus images for the case d=2, and brain connectivity measures from diffusion weighted MRI-data for the case d=3, extending the work of Bekkers et al
Stability metrics for optic radiation tractography: Towards damage prediction after resective surgery
No full textBackground An accurate delineation of the optic radiation (OR) using diffusion MR tractography may reduce the risk of a visual field deficit after temporal lobe resection. However, tractography is prone to generate spurious streamlines, which deviate strongly from neighboring streamlines and hinder a reliable distance measurement between the temporal pole and the Meyer's loop (ML-TP distance). New method Stability metrics are introduced for the automated removal of spurious streamlines near the Meyer's loop. Firstly, fiber-to-bundle coherence (FBC) measures can identify spurious streamlines by estimating their alignment with the surrounding streamline bundle. Secondly, robust threshold selection removes spurious streamlines while preventing an underestimation of the extent of the Meyer's loop. Standardized parameter selection is realized through test–retest evaluation of the variability in ML-TP distance. Results The variability in ML-TP distance after parameter selection was below 2 mm for each of the healthy volunteers studied (N = 8). The importance of the stability metrics is illustrated for epilepsy surgery candidates (N = 3) for whom the damage to the Meyer's loop was evaluated by comparing the pre- and post-operative OR reconstruction. The difference between predicted and observed damage is in the order of a few millimeters, which is the error in measured ML-TP distance. Comparison with existing method(s) The stability metrics are a novel method for the robust estimate of the ML-TP distance. Conclusions The stability metrics are a promising tool for clinical trial studies, in which the damage to the OR can be related to the visual field deficit that may occur after epilepsy surgery.</p
