waashk/dblp huggingface dataset:
non-uniform gradient prescription for precise angular measurements using dti. diffusion tensor imaging (dti) calculates a tensor for each voxel, representing the mean diffusive characteristics in volume-averaged tissue. gradients that phase-encode spins according to the amount of their diffusion are usually applied uniformly over a sphere during a dti procedure for minimal bias of tensor information. if prior knowledge of diffusion direction exists, the angular precision for determining the principle eigenvector of cylindrically-symmetric ("prolate") tensors can be improved by specifying gradients non-uniformly. improvements in precision of 30-40% can be achieved using a restricted band of zenith angle values for gradient directions. sensitivity to the a priori angular range of the principle eigenvector can be adjusted with the width of the band. simulations and phantom data are in agreement; a preliminary validation is presented.
automatic intra-operative generation of geometric left atrium/pulmonary vein models from rotational x-ray angiography. pre-procedural imaging with cardiac ct or mr has become popular for guiding complex electrophysiology procedures such as those used for atrial fibrillation ablation therapy. electroanatomical mapping and ablation within the left atrium and pulmonary veins (lapv) is facilitated using such data, however the pre-procedural anatomy can be quite different from that at the time of intervention. recently, a method for intra-procedural lapv imaging has been developed based on contrast-enhanced 3-d rotational x-ray angiography (3-d ra). these intraprocedural data now create a compelling need for rapid and automated extraction of the lapv geometry for catheter guidance. we present a new approach to automatic intra-procedural generation of lapv surfaces from 3-d ra volumes. using model-based segmentation, our technique is robust to imaging noise and artifacts typical of 3-d ra imaging, strongly minimizes the user interaction time required for segmentation, and eliminates inter-subject variability. our findings in 33 patients indicate that intra-procedural lapv surface models accurately represent the anatomy at the time of intervention and are comparable to pre-procedural models derived from cta or mra.
motion correction in respiratory gated cardiac pet/ct using multi-scale optical flow. respiratory motion is a source of degradation in positron emission tomography. as the patients cannot hold breath during the pet acquisition, spatial blurring and motion artifacts are unavoidable which may lead to wrong quantification of the data. a solution based on respiratory-gating and optical flow based correction of the pet data is proposed. this includes deformation of the ct data for accurate attenuation and listmode based reconstruction. all methods are applied to real patient data and are evaluated with respect to three criteria.
3d brain segmentation using active appearance models and local regressors. we describe an efficient and accurate method for segmenting sets of subcortical structures in 3d mr images of the brain. we first find the approximate position of all the structures using a global active appearance model (aam). we then refine the shape and position of each structure using a set of individual aams trained for each. finally we produce a detailed segmentation by computing the probability that each voxel belongs to the structure, using regression functions trained for each individual voxel. the models are trained using a large set of labelled images, using a novel variant of `groupwise' registration to obtain the necessary image correspondences. we evaluate the method on a large dataset, and demonstrate that it achieves results comparable with some of the best published.
fast musculoskeletal registration based on shape matching. this paper presents a new method for computing elastic and plastic deformations in the context of discrete deformable model-based registration. internal forces are estimated by averaging local transforms between reference and current particle positions. our technique can accommodate large non-linear deformations, and is unconditionally stable. moreover, it is simple to implement and versatile. we show how to tune model stiffness and computational cost, which is important for efficient registration, and demonstrate our technique in the complex problem of inter-patient musculoskeletal registration.
a statistical motion model based on biomechanical simulations for data fusion during image-guided prostate interventions. a method is described for generating a patient-specific, statistical motion model (smm) of the prostate gland. finite element analysis (fea) is used to simulate the motion of the gland using an ultrasound-based 3d fe model over a range of plausible boundary conditions and soft-tissue properties. by applying principal component analysis to the displacements of the fe mesh node points inside the gland, the simulated deformations are then used as training data to construct the smm. the smm is used to both predict the displacement field over the whole gland and constrain a deformable surface registration algorithm, given only a small number of target points on the surface of the deformed gland. using 3d transrectal ultrasound images of the prostates of five patients, acquired before and after imposing a physical deformation, to evaluate the accuracy of predicted landmark displacements, the mean target registration error was found to be less than 1.9mm.
modelling mammographic compression of the breast. we have developed a biomechanical model of the breast to simulate compression during mammographic imaging. the modelling framework was applied to a set of mr images of the breasts of a volunteer. images of the uncompressed breast were segmented into skin and pectoral muscle, from which a finite element (fe) mesh of the left breast was generated using a nonlinear geometric fitting process. the compression plates within the breast mr coil were used to compress the volunteer's breasts by 32% in the latero-medial direction and the compressed breasts were subsequently imaged using mri. the fe geometry of the uncompressed left breast was used to numerically simulate compression based on finite deformation elasticity coupled with contact mechanics, and individual-specific tissue properties. accuracy of the simulated fe model was analysed by comparing the predicted surface data, and locations of three internal features within the compressed breast, with the equivalent experimental observations. model predictions of the surface deformation yielded a rms error of 1.5 mm. the euclidean errors in predicting the locations of three internal features were 4.1 mm, 4.1 mm and 6.5 mm. whilst the model reliably reproduced the compressive deformation, further investigations are required in order to test the validity of the underlying modelling assumptions. a reliable biomechanical model will provide a multi-modality imaging registration tool to help identify potential tumours observed between mammograms and other imaging modalities such as mri or ultrasound.
optimal feature selection applied to multispectral fluorescence imaging. recent rapid developments in multi-modal optical imaging have created a significant clinical demand for its in vivo - in situ application. this offers the potential for real-time tissue characterization, functional assessment, and intra-operative guidance. one of the key requirements for in vivo consideration is to minimise the acquisition window to avoid tissue motion and deformation, whilst making the best use of the available photons to account for correlation or redundancy between different dimensions. the purpose of this paper is to propose a feature selection framework to identify the best combination of features for discriminating between different tissue classes such that redundant or irrelevant information can be avoided during data acquisition. the method is based on a bayesian framework for feature selection by using the receiver operating characteristic curves to determine the most pertinent data to capture. this represents a general technique that can be applied to different multi-modal imaging modalities and initial results derived from phantom and ex vivo tissue experiments demonstrate the potential clinical value of the technique.
segmentation of vessels cluttered with cells using a physics based model. segmentation of vessels in biomedical images is important as it can provide insight into analysis of vascular morphology, topology and is required for kinetic analysis of flow velocity and vessel permeability. intravital microscopy is a powerful tool as it enables in vivo imaging of both vasculature and circulating cells. however, the analysis of vasculature in those images is difficult due to the presence of cells and their image gradient. in this paper, we provide a novel method of segmenting vessels with a high level of cell related clutter. a set of virtual point pairs ("vessel probes") are moved reacting to forces including vessel vector flow (vvf) and vessel boundary vector flow (vbvf) forces. incorporating the cell detection, the vvf force attracts the probes toward the vessel, while the vbvf force attracts the virtual points of the probes to localize the vessel boundary without being distracted by the image features of the cells. the vessel probes are moved according to newtonian physics reacting to the net of forces applied on them. we demonstrate the results on a set of five real in vivo images of liver vasculature cluttered by white blood cells. when compared against the ground truth prepared by the technician, the root mean squared error (rmse) of segmentation with vvf and vbvf was 55% lower than the method without vvf and vbvf.
an atlas-based segmentation propagation framework using locally affine registration - application to automatic whole heart segmentation. in this paper, we present a novel registration algorithm for locally affine registrations. this method preserves the anatomical and intensity class relationships between the local regions. a regularisation procedure is used to maintain a global diffeomorphic transformation. combined with a novel generic method for accurately inverting the final deformation field, we include our techniques within an atlas-based segmentation propagation framework. we applied our method to automatically segment the whole heart from cardiac magnetic resonance images from a cohort of 18 volunteers (acquisition resolution 2 × 2 × 2 mm). the results show that the proposed method provides a robust initialisation for the atlas-based segmentation propagation framework refined with a fluid registration. we validated our approach against other registration strategies, and demonstrated that we improved the accuracy of the whole heart segmentations (1.8 ± 0.42 mm).
simulations of needle insertion by using a eulerian hydrocode fem and the experimental validations. in this paper, simulations for needle insertion were performed by using a novel eulerian hydrocode fem, which was adaptive for large deformation and tissue fracture. we also performed experiments for the same needle insertion with silicon rubbers and needles, which had conical tips of different angles in order to investigate the accuracy of the simulations. the resistance forces in the simulations accurately followed those in the experiments until the conical portion of the needle was inside the rubbers, and the validation of the eulerian hydrocode was revealed. however, the present simulation showed that after the conical portion was inside the tissue, the simulated resistance forces became lower than the experimental ones. the proportional increase of the friction forces and the roughly flatness of the tip force along the time were simulated. it was predicted that the tightening force along the needle side was underestimated.
image thickness correction for navigation with 3d intra-cardiac ultrasound catheter. in this paper we present an algorithm to correct 3d reconstruction errors of 3d ultrasound catheter caused by ultrasound image thickness. we also provide a method to quickly measure ultrasound image plane's thickness. with thickness correction registration accuracy of navigation system using 3d ultrasound catheters can be improved by 20%.
modeling and online recognition of surgical phases using hidden markov models. the amount of signals that can be recorded during a surgery, like tracking data or state of instruments, is constantly growing. these signals can be used to better understand surgical workflow and to build surgical assist systems that are aware of the current state of a surgery. this is a crucial issue for designing future systems that provide context-sensitive information and user interfaces.in this paper, hidden markov models (hmm) are used to model a laparoscopic cholecystectomy. seventeen signals, representing tool usage, from twelve surgeries are used to train the model. the use of a model merging approach is proposed to build the hmm topology and compared to other methods of initializing a hmm. the merging method allows building a model at a very fine level of detail that also reveals the workflow of a surgery in a human-understandable way. results for detecting the current phase of a surgery and for predicting the remaining time of the procedure are presented.
cardiac medial modeling and time-course heart wall thickness analysis. the medial model is a powerful shape representation method that models a 3d object by explicitly defining its skeleton (medial axis) and deriving the boundary geometry according to medial geometry. it has been recently extended to model complex shapes with multi-figures, i.e., shapes whose skeletons can not be described by a single sheet in 3d. this paper applied the medial model to a 2-chamber heart data set consisting of 428 cardiac shapes from 90 subjects. the results show that the medial model can capture the heart shape accurately. to demonstrate the usage of the medial model, the changes of the heart wall thickness over time are analyzed. we calculated the mean heart wall thickness map of 90 subjects for different phases of the cardiac cycle, as well as the mean thickness change between phases.
optimized conformal parameterization of cortical surfaces using shape based matching of landmark curves. in this work, we find meaningful parameterizations of cortical surfaces utilizing prior anatomical information in the form of anatomical landmarks (sulci curves) on the surfaces. specifically we generate close to conformal parametrizations that also give a shape-based correspondence between the landmark curves. we propose a variational energy that measures the harmonic energy of the parameterization maps, and the shape dissimilarity between mapped points on the landmark curves. the novelty is that the computed maps are guaranteed to give a shape-based diffeomorphism between the landmark curves. we achieve this by intrinsically modelling our search space of maps as flows of smooth vector fields that do not flow across the landmark curves, and by using the local surface geometry on the curves to define a shape measure. such parameterizations ensure consistent correspondence between anatomical features, ensuring correct averaging and comparison of data across subjects. the utility of our model is demonstrated in experiments on cortical surfaces with landmarks delineated, which show that our computed maps give a shape-based alignment of the sulcal curves without significantly impairing conformality.
label space: a coupled multi-shape representation. richly labeled images representing several sub-structures of an organ occur quite frequently in medical images. for example, a typical brain image can be labeled into grey matter, white matter or cerebrospinal fluid, each of which may be subdivided further. many manipulations such as interpolation, transformation, smoothing, or registration need to be performed on these images before they can be used in further analysis. in this work, we present a novel multi-shape representation and compare it with the existing representations to demonstrate certain advantages of using the proposed scheme. specifically, we propose label space, a representation that is both flexible and well suited for coupled multi-shape analysis. under this framework, object labels are mapped to vertices of a regular simplex, e.g the unit interval for two labels, a triangle for three labels, a tetrahedron for four labels, etc. this forms the basis of a convex linear structure with the property that all labels are equally spaced. we will demonstrate that this representation has several desirable properties: algebraic operations may be performed directly, label uncertainty is expressed equivalently as a weighted mixture of labels or in a probabilistic manner, and interpolation is unbiased toward any label or the background. in order to demonstrate these properties, we compare label space to signed distance maps as well as other implicit representations in tasks such as smoothing, interpolation, registration, and principal component analysis.
real-time simulation of 4d lung tumor radiotherapy using a breathing model. in this paper, we present a real-time simulation and visualization framework that models a deformable surface lung model with tumor, simulates the tumor motion and predicts the amount of radiation doses that would be deposited in the moving lung tumor during the actual delivery of radiation. the model takes as input a subject-specific 4d computed tomography (4d ct) of lungs and computes a deformable lung surface model by estimating the deformation properties of the surface model using an inverse dynamics approach. once computed, the deformable model is used to simulate and visualize lung tumor motion that would occur during radiation therapy accounting for variations in the breathing pattern. a radiation treatment plan for the lung tumor is developed using one of the 4d ct phases. during the simulation of radiation delivery, the dose on the lung tumor is computed for each beam independently.
active volume models with probabilistic object boundary prediction module. we propose a novel active volume model (avm) which deforms in a free-form manner to minimize energy. unlike snakes and level-set active contours which only consider curves or surfaces, the avm is a deforming object model that has both boundary and an interior area. when applied to object segmentation and tracking, the model alternates between two basic operations: deform according to current object prediction, and predict according to current appearance statistics of the model. the probabilistic object prediction module relies on the bayesian decision rule to separate foreground (i.e. object represented by the model) and background. optimization of the model is a natural extension of the snakes model so that region information becomes part of the external forces. the avm thus has the efficiency of snakes while having adaptive region-based constraints. segmentation results, validation, and comparison with gvf snakes and level set methods are presented for experiments on noisy 2d/3d medical images.
fieldmap-free retrospective registration and distortion correction for epi-based functional imaging. we describe a method for correcting the distortions present in echo planar images (epi) and registering the epi to structural mri. a fieldmap is predicted from an air / tissue segmentation of the mri using a perturbation method and subsequently used to unwarp the epi data. shim and other missing parameters are estimated by registration. we obtain results that are similar to those obtained using fieldmaps, however neither fieldmaps, nor knowledge of shim coefficients is required.
parallelized hybrid tgrappa reconstruction for real-time interactive mri. real-time parallel mri reconstruction was demonstrated using a hybrid implementation of the tgrappa algorithm. the grappa coefficients were calculated in k-space and applied in the image domain after appropriate transformation, thereby achieving improved speed and excellent image quality. adaptive b1-weighted combining of the per coil images permitted use of pre-calculated composite image domain weights providing significant decrease in computation. the weight calculation was decoupled from the real-time image reconstruction as a parallel processing thread which was updated in an adaptive manner to speed convergence in the event of interactive change in scan plane. the computation was parallelized and implemented on a general purpose multi-core architecture. reconstruction speeds of 65-70 frames per second were achieved with a matrix of 192x144 with 15 coils.
symmetric nonrigid image registration: application to average brain templates construction. image registration aims at estimating a consistent mapping between two images. common techniques consist in choosing arbitrarily one image as a reference image and the other one as a floating image, thus leading to the estimation of inconsistent mappings. we present a symmetric formulation of the registration problem that maps the two images in a common coordinate system halfway between them. this framework has been considered to devise an efficient strategy for mapping a large set of images in a common coordinate system. some results are presented in the context of 3-d nonrigid brain mr image registration for the construction of average brain templates.
weight preserving image registration for monitoring disease progression in lung ct. we present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. a free-form image registration technique is used to match a baseline 3d ct lung scan onto a following scan. areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. to account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between ct scans and may result in a more sensitive measure of disease progression than standard quantitative ct measures.
a new method for creating electrophysiological maps for dbs surgery and their application to surgical guidance. electrophysiological maps based on a gaussian kernel have been proposed as a means to visualize response to stimulation in deep brain stimulation (dbs) surgeries. however, the gaussian model does not represent the underlying physiological phenomenon produced by stimulation. we propose a new method to create physiological maps, which relies on spherical shell kernels. we compare our new maps to those created with gaussian kernels and show that, on simulated data, this new approach produces more realistic maps. experiments we have performed with real patient data show that our new maps correlate well with the underlying anatomy. finally, we present preliminary results on an ongoing study assessing the value of these maps as pre-operative planning and intra-operative guidance tools.