waashk/dblp huggingface dataset:
a distributed spatio-temporal eeg/meg inverse solver. we propose a novel ¿1¿2-norm inverse solver for estimating the sources of eeg/meg signals. based on the standard ¿1-norm inverse solver, the proposed sparse distributed inverse solver integrates the ¿1-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and "spiky" reconstructed signals often produced by the original solvers. the joint spatio-temporal model leads to a cost function with an ¿1¿2-norm regularizer whose minimization can be reduced to a convex second-order cone programming problem and efficiently solved using the interior-point method. validation with simulated and real meg data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. furthermore, the ¿1¿2-norm solver achieves fewer false positives and a better representation of the source locations than the conventional ¿2 minimum-norm estimates.
robotic assistant for transperineal prostate interventions in 3t closed mri. numerous studies have demonstrated the efficacy of image-guided needle-based therapy and biopsy in the management of prostate cancer. the accuracy of traditional prostate interventions performed using transrectal ultrasound (trus) is limited by image fidelity, needle template guides, needle deflection and tissue deformation. magnetic resonance imaging (mri) is an ideal modality for guiding and monitoring such interventions due to its excellent visualization of the prostate, its sub-structure and surrounding tissues. we have designed a comprehensive robotic assistant system that allows prostate biopsy and brachytherapy procedures to be performed entirely inside a 3t closed mri scanner. we present a detailed design of the robotic manipulator and an evaluation of its usability and mr compatibility.
anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: application to xmr interventional imaging. cardiac arrhythmias are increasingly being treated using ablation procedures. development of fast electrophysiological models and estimation of parameters related to conduction pathologies can aid in the investigation of better treatment strategies during radio-frequency ablations. we present a fast electrophysiological model incorporating anisotropy of the cardiac tissue. a global-local estimation procedure is also outlined to estimate a hidden parameter (apparent electrical conductivity) present in the model. the proposed model is tested on synthetic and real data derived using xmr imaging. we demonstrate a qualitative match between the estimated conductivity parameter and possible pathology locations. this approach opens up possibilities to directly integrate modelling in the intervention room.
non-rigid image registration using graph-cuts. non-rigid image registration is an ill-posed yet challenging problem due to its supernormal high degree of freedoms and inherent requirement of smoothness. graph-cuts method is a powerful combinatorial optimization tool which has been successfully applied into image segmentation and stereo matching. under some specific constraints, graph-cuts method yields either a global minimum or a local minimum in a strong sense. thus, it is interesting to see the effects of using graph-cuts in non-rigid image registration. in this paper, we formulate non-rigid image registration as a discrete labeling problem. each pixel in the source image is assigned a displacement label (which is a vector) indicating which position in the floating image it is spatially corresponding to. a smoothness constraint based on first derivative is used to penalize sharp changes in displacement labels across pixels. the whole system can be optimized by using the graph-cuts method via alpha-expansions. we compare 2d and 3d registration results of our method with two state-of-the-art approaches. it is found that our method is more robust to different challenging non-rigid registration cases with higher registration accuracy.
probabilistic speckle decorrelation for 3d ultrasound. recent developments in freehand 3d ultrasound (us) have shown how image registration and speckle decorrelation methods can be used for 3d reconstruction instead of relying on a tracking device. estimating elevational separation between untracked us images using speckle decorrelation is error prone due to the uncertainty that plagues the correlation measurements. in this paper, using maximum entropy estimation methods, the uncertainty is directly modeled from the calibration data normally used to estimate an average decorrelation curve. multiple correlation measurements can then be fused within a maximum likelihood estimation framework in order to reduce the drift in elevational pose estimation over large image sequences. the approach is shown to be effective through empirical results on simulated and phantom us data.
shape analysis using a point-based statistical shape model built on correspondence probabilities. a fundamental problem when computing statistical shape models is the determination of correspondences between the instances of the associated data set. often, homologies between points that represent the surfaces are assumed which might lead to imprecise mean shape and variability results. we propose an approach where exact correspondences are replaced by evolving correspondence probabilities. these are the basis for a novel algorithm that computes a generative statistical shape model. we developed an unified map framework to compute the model parameters ('mean shape' and 'modes of variation') and the nuisance parameters which leads to an optimal adaption of the model to the set of observations. the registration of the model on the instances is solved using the expectation maximization - iterative closest point algorithm which is based on probabilistic correspondences and proved to be robust and fast. the alternated optimization of the map explanation with respect to the observation and the generative model parameters leads to very efficient and closed-form solutions for (almost) all parameters. experimental results on brain structure data sets demonstrate the efficiency and well-posedness of the approach. the algorithm is then extended to an automatic classification method using the k-means clustering and applied to synthetic data as well as brain structure classification problems.
automatic inference of sulcus patterns using 3d moment invariants. the goal of this work is the automatic inference of frequent patterns of the cortical sulci, namely patterns that can be observed only for a subset of the population. the sulci are detected and identified using brainvisa open software. then, each sulcus is represented by a set of shape descriptors called the 3d moment invariants. unsupervised agglomerative clustering is performed to define the patterns. a ratio between compactness and contrast among clusters is used to select the best patterns. a pattern is considered significant when this ratio is statistically better than the ratios obtained for clouds of points following a gaussian distribution. the patterns inferred for the left cingulate sulcus are consistent with the patterns described in the atlas of ono.
eeg to mri registration based on global and local similarities of mri intensity distributions. in this paper, a novel method for eeg to mri registration is proposed. initial registration is achieved by extracting and matching symmetry planes of mri and eeg data, followed by iterative registration based on minimizing a cost function. comparison of the intensity distributions of the whole mr image and mri voxels around a head surface point yields global similarities, while the comparison of intensity distributions of mri voxels around corresponding eeg points, which reflects the head's sagittal symmetry, yields local similarities. therefore, when the eeg points are registered to the mr image, maximal global and local similarities should be obtained. the cost function, incorporating global and local similarities, was the sum of kullback-leibler divergences between corresponding intensity distributions. the proposed method was evaluated on clinical mri data with simulated eeg data, yielding mean registration error of 0.48 ±0.33 mm, while with real eeg data an average root-mean-square point-to-surface error of 2.27 ±0.02 mm was obtained.
non-rigid surface registration using spherical thin-plate splines. accurate registration of cortical structures plays a fundamental role in statistical analysis of brain images across population. this paper presents a novel framework for the non-rigid intersubject brain surface registration, using conformal structure and spherical thin-plate splines. by resorting to the conformal structure, complete characteristics regarding the intrinsic cortical geometry can be retained as a mean curvature function and a conformal factor function defined on a canonical, spherical domain. in this transformed space, spherical thin-plate splines are firstly used to explicitly match a few prominent homologous landmarks, and in the meanwhile, interpolate a global deformation field. a post-optimization procedure is then employed to further refine the alignment of minor cortical features based on the geometric parameters preserved on the domain. our experiments demonstrate that the proposed framework is highly competitive with others for brain surface registration and population-based statistical analysis. we have applied our method in the identification of cortical abnormalities in pet imaging of patients with neurological disorders and accurate results are obtained.
virtually extended surgical drilling device: virtual mirror for navigated spine surgery. this paper introduces a new method for navigated spine surgery using a stereoscopic video see-through head-mounted display (hmd) and an optical tracking system. vertebrae are segmented from volumetric ct data and visualized in-situ. a surgical drilling device is virtually extended with a mirror for intuitive planning of the drill canal, control of drill direction and insertion depth. the first designated application for the virtually extended drilling device is the preparation of canals for pedicle screw implantation in spine surgery. the objective of surgery is to install an internal fixateur for stabilization of injured vertebrae. we invited five surgeons of our partner clinic to test the system with realistic replica of lumbar vertebrae and compared the new approach with the classical, monitor-based navigation system providing three orthogonal slice views on the operation site. we measured time of procedure and scanned the drilled vertebrae with ct to verify accuracy of drilling.
automatic dry eye detection. dry eye syndrome is a common disease in the western world, with effects from uncomfortable itchiness to permanent damage to the ocular surface. nevertheless, there is still no objective test that provides reliable results. we have developed a new method for the automated detection of dry areas in videos taken after instilling fluorescein in the tear film. the method consists of a multi-step algorithm to first locate the iris in each image, then align the images and finally analyze the aligned sequence in order to find the regions of interest. since the fluorescein spreads on the ocular surface of the eye the edges of the iris are fuzzy making the detection of the iris challenging. we use ransac to first detect the upper and lower eyelids and then the iris. then we align the images by finding differences in intensities at different scales and using a least squares optimization method (levenberg-marquardt), to overcome the movement of the iris and the camera. the method has been tested on videos taken from different patients. it is demonstrated to find the dry areas accurately and to provide a measure of the extent of the disease.
application of open source image guided therapy software in mr-guided therapies. we present software engineering methods to provide free open-source software for mr-guided therapy. we report that graphical representation of the surgical tools, interconnectively with the tracking device, patient-to-image registration, and mri-based thermal mapping are crucial components of mr-guided therapy in sharing such software. software process includes a network-based distribution mechanism by multi-platform compiling tool cmake, cvs, quality assurance software dart. we developed six procedures in four separate clinical sites using proposed software engineering and process, and found the proposed method is feasible to facilitate multicenter clinical trial of mr-guided therapies. our future studies include use of the software in non-mr-guided therapies.
analysis of surfaces using constrained regression models. we present a study of the relationship between the changes in the shape of the human ear due to jaw movement and acoustical feedback (af) in hearing aids. in particular, we analyze the deformation field of the outer ear associated with the movement of the mandible (jaw bone) to understand its effect on af and identify local regions that play a significant role. our data contains ear impressions of 42 hearing aid users, in two different positions: open and closed mouth, and survey data including information about experienced discomfort due to af. we use weighted support vector machines (wsvm) to investigate the separation between the presence and lack of af and achieve classification accuracy of 80% based on the deformation field. to robustly localize the regions of the deformation field that significantly contribute to af we employ logistic regression penalized with elastic net (en). by visualizing the selected variables on the mean surface, we provide clinical interpretations of the results.
mr brain tissue classification using an edge-preserving spatially variant bayesian mixture model. in this paper, a spatially constrained mixture model for the segmentation of mr brain images is presented. the novelty of this work is an edge-preserving smoothness prior which is imposed on the probabilities of the voxel labels. this prior incorporates a line process, which is modeled as a bernoulli random variable, in order to preserve edges between tissues. the main difference with other, state of the art methods imposing priors, is that the constraint is imposed on the probabilities of the voxel labels and not onto the labels themselves. inference of the proposed bayesian model is obtained using variational methodology and the model parameters are computed in closed form. numerical experiments are presented where the proposed model is favorably compared to state of the art brain segmentation methods as well as to a spatially varying gaussian mixture model.
real-time tracking of the left ventricle in 3d echocardiography using a state estimation approach. in this paper we present a framework for real-time tracking of deformable contours in volumetric datasets. the framework supports composite deformation models, controlled by parameters for contour shape in addition to global pose. tracking is performed in a sequential state estimation fashion, using an extended kalman filter, with measurement processing in information space to effectively predict and update contour deformations in real-time. a deformable b-spline surface coupled with a global pose transform is used to model shape changes of the left ventricle of the heart. successful tracking of global motion and local shape changes without user intervention is demonstrated on a dataset consisting of 21 3d echocardiography recordings. real-time tracking using the proposed approach requires a modest cpu load of 13% on a modern computer. the segmented volumes compare to a semi-automatic segmentation tool with 95% limits of agreement in the interval 4.1 ± 24.6 ml (r = 0.92).
riemannian framework for estimating symmetric positive definite 4th order diffusion tensors. dti is an important tool to investigate the brain in vivo and non-invasively in spite of its shortcomings in regions of fiber-crossings. hardi models such as qbi and higher order tensors (hot) were invented to overcome this shortcoming. hots, however, have not been explored extensively even though sophisticated estimation schemes were developed for dti that guarantee positive diffusivity, such as the riemannian framework. positive diffusivity is an important constraint in diffusion mri since it represents the physical phenomenon of molecular diffusion. it seems apt, to leverage the work done on dti, to apply the positivity constraint to the hot model. we, therefore, propose to extend the riemannian framework from dti to the space of 4th order diffusion tensors. we also review the existing methods for estimating 4th order diffusion tensors and compare all methods on synthetic, phantom and real datasets extensively to test for robustness and speed. our contributions for extending the riemannian framework from dti to estimating 4th order diffusion tensors guarantees positive diffusivity, is robust, is fast, and can be used to discern multiple fiber directions.
topology preserving warping of binary images: application to atlas-based skull segmentation. lots of works have been recently carried out in the field of non-rigid registration to ensure the estimation of one-to-one mappings. however, warping a binary image with such transformations may alter its discrete topological properties if common resampling strategies are considered. this paper proposes an original method for warping a binary image according to some continuous and bijective mapping, while preserving its discrete topological properties. results obtained in the context of atlas-based segmentation highlight the interest of the approach. indeed, the method has been successfully applied to the segmentation of skull structures from a database of 15 ct-scans, providing both geometrically and topologically satisfactory results.
global medical shape analysis using the laplace-beltrami spectrum. this paper proposes to use the laplace-beltrami spectrum (lbs) as a global shape descriptor for medical shape analysis, allowing for shape comparisons using minimal shape preprocessing: no registration, mapping, or remeshing is necessary. the discriminatory power of the method is tested on a population of female caudate shapes of normal control subjects and of subjects with schizotypal personality disorder.
construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from ct images. hierarchical multi-organ statistical atlases are constructed with the aim of achieving fully automated segmentation of the liver and related organs from computed tomography images. constraints on inter-relations among organs are embedded in hierarchical organization of probabilistic atlases (pas) and statistical shape models (ssms). hierarchical pas are constructed based on the hierarchical nature of inter-organ relationships. multi-organ ssms (mo-ssms) are combined with previously proposed single-organ multi-level ssms (ml-ssms). a hierarchical segmentation procedure is then formulated using the constructed hierarchical atlases. the basic approach consists of hierarchical recursive processes of initial region extraction using pas and subsequent refinement using ml/mo-ssms. the experimental results show that segmentation accuracy of the liver was improved by incorporating constraints on inter-organ relationships.
a new benchmark for shape correspondence evaluation. this paper introduces a new benchmark study of evaluating landmark-based shape correspondence used for statistical shape analysis. different from previous shape-correspondence evaluation methods, the proposed benchmark first generates a large set of synthetic shape instances by randomly sampling a specified ground-truth statistical shape model. we then run the test shape-correspondence algorithms on these synthetic shape instances to construct a new statistical shape model. we finally introduce a new measure to describe the difference between this newly constructed statistical shape model and the ground truth. this new measure is then used to evaluate the performance of the test shape-correspondence algorithm. by introducing the ground-truth statistical shape model, we believe the proposed benchmark allows for a more objective evaluation of the shape correspondence than those that do not specify any ground truth.
modelling anisotropic viscoelasticity for real-time soft tissue simulation. previously almost all biomechanically-based time-critical surgical simulation has ignored the well established features of tissue mechanical response of anisotropy and time-dependence. we address this issue by presenting an efficient solution procedure for anisotropic visco-hyperelastic constitutive models which allows use of these in nonlinear explicit dynamic finite element algorithms. we show that the procedure allows incorporation of both anisotropy and viscoelasticity for as little as 5.1% additional cost compared with the usual isotropic elastic models. when combined with high performance gpu execution the complete framework is suitable for time-critical simulation applications such as interactive surgical simulation and intraoperative image registration.
automatic mitral valve inflow measurements from doppler echocardiography. doppler echocardiography is widely used for functional assessment of heart valves such as mitral valve. in current clinical work flow, to extract doppler measurements, the envelopes of acquired doppler spectra are manually traced. we propose a robust algorithm for automatically tracing the envelopes of mitral valve inflow doppler spectra, which exhibit a large amount of variations in envelope shape and image appearance due to various disease conditions, patient/sonographer/instrument differences, etc. the algorithm is learning-based and capable of fully automatic detection and segmentation of the mitral inflow structures. experiments show that the algorithm, running within one second, yields comparable performance to experts.
a training system for ultrasound-guided needle insertion procedures. needle placement into a patient body under guidance of ultrasound is a frequently performed procedure in clinical practice. safe and successful performance of such procedure requires a high level of spatial reasoning and hand-eye co-ordination skills, which must be developed through intensive practice. in this paper we present a training system designed to improve the skills of interventional radiology trainees in ultrasound-guided needle placement procedures. key issues involved in the system include surface and volumetric registration, solid texture modelling, spatial calibration, and real-time synthesis and rendering of ultrasound images. moreover, soft tissue deformation caused by the needle movement and needle cutting is realised using a mass-spring-model approach. these have led to a realistic ultrasound simulation system, which has been shown to be a useful tool for the training of needle insertion procedures. preliminary results of a construct evaluation study indicate the effectiveness and usefulness of the developed training system.