waashk/dblp huggingface dataset:
discovering modes of an image population through mixture modeling. we present icluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. the output is a small number of template images that represent different modes in a population. this is in contrast with traditional approaches that assume a single template to construct atlases. we validate and explore the algorithm in two experiments. first, we employ icluster to partition a data set of 416 whole brain mr volumes of subjects aged 18-96 years into three sub-groups, which mainly correspond to age groups. the templates reveal significant structural differences across these age groups that confirm previous findings in aging research. in the second experiment, we run icluster on a group of 30 patients with dementia and 30 age-matched healthy controls. the algorithm produced three modes that mainly corresponded to a sub-population of healthy controls, a sub-population of patients with dementia and a mixture group that contained both types. these results suggest that the algorithm can be used to discover sub-populations that correspond to interesting structural or functional "modes."
atlas-based auto-segmentation of head and neck ct images. treatment planning for high precision radiotherapy of head and neck (h&n) cancer patients requires accurate delineation of many structures and lymph node regions. manual contouring is tedious and suffers from large inter- and intra-rater variability. to reduce manual labor, we have developed a fully automated, atlas-based method for h&n ct image segmentation that employs a novel hierarchical atlas registration approach. this registration strategy makes use of object shape information in the atlas to help improve the registration efficiency and robustness while still being able to account for large inter-subject shape differences. validation results showed that our method provides accurate segmentation for many structures despite difficulties presented by real clinical data. comparison of two different atlas selection strategies is also reported.
fast motion tracking of tagged mri using angle-preserving meshless registration. fast tracking of motion is the key step towards tagged mri-based quantitative cardiac analysis. existing motion tracking approaches, including the widely used harp method, are either time consuming or qualitatively inconsistent, or both. we present in this paper a new fast motion tracking method based on a meshless kernel. for mr image sequences containing multiple image frames, tag intersections are automatically detected in all frames and indexed in the first frame. then a thin plate spline approach is used to establish a point-to-point correspondence between tag intersections in the initial and the current frame. lastly, we use a meshless registration kernel to generate a dense displacement map that minimizes the residual of sparse motion at intersections. to further improve the motion tracking, we develop a special technique to preserve tangential angles of tags at tag intersections. we tested our new method on both numerical phantoms and in vivo heart data. the motion tracking results are evaluated against the ground truth and manually drawn tags. clinical application potential is demonstrated by cardiac strain analysis based on the proposed methodology.
an active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on mri: method and validation. this paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (stn) targeting in deep brain stimulation (dbs) surgery. in a previous work, we demonstrated that the stn position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. a stn targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. our simultaneous segmentation and registration model shows mean stn localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.
path planning and workspace determination for robot-assisted insertion of steerable electrode arrays for cochlear implant surgery. in previous works, the authors showed that using robot-assisted steerable electrode array insertions can significantly reduce the insertion forces compared to non-steerable electrode arrays. in addition to steering the electrode array, it is possible to change its angle of approach with respect to the scala tympani. this paper focuses on determining the relevance of changing the angle of approach of the electrode array by comparing steerable electrode array insertions using a two degrees-of-freedom (dof) robot versus a four dof robot. optimal insertion path planning strategies are presented for both two and four dof insertions. simulation results and experiments show that the four dof insertions can improve over two dof insertions. moreover, changing the angle of approach can further reduce the insertion forces. the simulation results also provide the workspace requirements for designing a custom parallel robot for robot-assisted cochlear implant surgery.
a theoretical comparison of different target registration error estimators. estimation of target registration error (tre), a common measure of the registration accuracy, is an important issue in computer assisted surgeries. within the last decade, several new approaches have been developed to estimate either the mean squared value of tre or the distribution of tre under different noise conditions. in this paper, we theoretically demonstrate that all the proposed algorithms converge to a general maximum likelihood (ml) solution. numerical simulations are performed to validate our derivations. using experimentally measured fiducial localization error, we provide an example of tre prediction in the presence of anisotropic noise.
needle insertion study using ultrasound-based 2d motion tracking. needle insertion simulators are in demand to train physicians for surgical procedures such as prostate brachytherapy. in order to design a needle insertion simulator, a needle-tissue interaction model is necessary. such a model can be identified using force and displacement data measured during the insertion of the needle. in this paper, an experiment is conducted in which a needle is inserted into a two-layered pvc phantom, while the needle position and insertion forces are measured and the tissue is imaged using a trans-rectal probe in the sagittal plane. a 2d block matching algorithm is used to estimate tissue deformation from the envelope of the recorded radio-frequency signals. this algorithm can be used to estimate the rotation and displacement of the prostate during prostate brachytherapy. the block matching method was validated in an independent experiment. with the measured force, needle position and tissue displacements, a finite element simulation was conducted to identify the parameters of a needle-tissue force distribution model and young's moduli of each part of the tissue phantom.
toward a flexible and portable ct scanner. the very hot and power-hungry x-ray filaments in today's computed tomography (ct) scanners constrain their design to be big and stationary. what if we built a ct scanner that could be deployed at the scene of a car accident to acquire tomographic images before moving the victim? recent developments in nanotechnology have shown that carbon nanotubes can produce x-rays at room temperature, and with relatively low power needs. we propose a design for a portable and flexible ct scanner made up of an addressable array of tiny x-ray emitters and detectors. in this paper, we outline a basic design, propose a strategy for reconstruction, and demonstrate the concept using a software simulation of the scanner. we also raise a number of issues that still need to be overcome to build such a scanner.
real-time liver motion compensation for mrgfus. mr-guided focused ultrasound (mrgfus) is a non-invasive method by which tissue is ablated using ultrasound energy focused on a point. the procedure has proven effective for stationary targets (e.g. uterine fibroids) but has not yet been used for liver lesion treatment due to organ motion. we describe a method to compensate for organ motion to enable continuous application of ultrasound energy in the presence of target movement in the liver. the method involves tracking several salient features (typically blood vessels) in the vicinity of the target location. the location of the target point(s) themselves are updated using a thin plate spline (tps) interpolation scheme. we demonstrate sub-pixel tracking accuracy on synthetic sequences and additionally show results on mri sequences acquired on human subjects. per-feature tracking times were measured to be 5.7ms with a standard deviation of 1.6ms, sufficient for real-time use.
fast, adaptive expectation-maximization alignment for cryo-em. cryo-em is a method for reconstructing 3d structure of proteins without crystallization. the expectation-maximization (em) algorithm is used in the alignment step of cryo-em reconstructions. the em step is often a serious computational bottleneck for 3d reconstructions. this paper proposes a computationally adaptive version of the em algorithm that speeds up the algorithm by a factor of 20 ¿ 30. experiments with noisy real-world data are included to show that the algorithm achieves this speedup without any significant loss of accuracy. such speed ups are significant, allowing the reconstruction to converge in cpu-days rather than cpu-months.
3d dendrite reconstruction and spine identification. in neuron-biology, 3d neuron dendrite reconstruction followed by spine identification is indispensable for the study of neuronal functions and biophysical properties. in this paper, we propose an automatic dendrite reconstruction method to with a surface representation of the neuron on the basis of a novel level set approach. our novel level set approach can effectively tackle the problem of segmentation under blurring and intensity in-homogeneity. then spines are detected based on dendrite medial axis and a label-based thinning strategy is proposed to accurately extract the dendrite skeleton for spine identification. experimental results reveal that our method works well.
real-time simulation of medical ultrasound from ct images. medical ultrasound interpretation requires a great deal of experience. real-time simulation of medical ultrasound provides a cost-effective tool for training and easy access to a variety of cases and exercises. however, fully synthetic and realistic simulation of ultrasound is complex and extremely time-consuming. in this paper, we present a novel method for simulation of ultrasound images from 3d ct scans by breaking down the computations into a preprocessing and a run-time phase. the preprocessing phase produces detailed fixed-view 3d scattering images and the run-time phase generates view-dependent ultrasonic artifacts for a given aperture geometry and position within a volume of interest. we develop a simple acoustic model of the ultrasound for the run-time phase, which produces realistic ultrasound images in real-time when combined with the previously computed scattering image.
using curve-fitting of curvilinear features for assessing registration of clinical neuropathology with in vivo mri. traditional neuropathological examination provides information about neurological disease or injury of a patient at a high-resolution level. correlating this type of post mortem diagnosis with in vivo image data of the same patient acquired by non-invasive tomographic scans greatly complements the interpretation of any disease or injury. we present the validation of a registration method for correlating macroscopic pathological images with mr images of the same patient. this also allows for 3-d mapping of the distribution of pathological changes throughout the brain. as the validation deals with datasets of widely differing sampling, we propose a method using smooth curvilinear anatomical features in the brain which allows interpolation between wide-spaced samples. curvilinear features are common anatomically, and if selected carefully have the potential to allow determination of the accuracy of co-registration across large areas of a volume of interest.
a nonrigid image registration framework for identification of tissue mechanical parameters. we present a modular framework for mechanically regularized nonrigid image registration of 3d ultrasound and for identification of tissue mechanical parameters. mechanically regularized deformation fields are computed from sparsely estimated local displacements. we enforce image-based local motion estimates by applying concentrated forces at mesh nodes of a mechanical finite-element model. the concentrated forces are generated by the elongation of regularization springs connected to the mesh nodes as their free ends are displaced according to local motion estimates. the regularization energy corresponding to the potential energy stored in the springs is minimized when the mechanical response of the model matches the observed response of the organ. we demonstrate that this technique is suitable for identification of material parameters of a nonlinear viscoelastic liver model and demonstrate its benefits over traditional indentation methods in terms of improved volumetric agreement between the model response and the experiment.
mr navigated breast surgery: method and initial clinical experience. 3d dynamic contrast enhanced magnetic resonance (mr) images may help to reduce the high re-excision rate associated with breast conserving surgery. however these images are acquired prone, whilst surgery is performed supine which results in a large deformation that limits their usefulness. we describe here a registration technique based on a biomechanical model to account for soft tissue deformation between prone mr imaging and surgery. the accuracies of the individual registration steps are assessed off-line. we then report our first clinical experience with an image-guided surgery system which incorporates these algorithms. the system's accuracy is assessed against tracked ultrasound images, and is determined to be around 5mm for this case.
evaluation of rigid and non-rigid motion compensation of cardiac perfusion mri. although the evaluation of cardiac perfusion using mri could be of crucial importance for the diagnosis of ischemic heart diseases, it is still not a routinely used technique. the major difficulty is that mr perfusion images are often corrupted by inconsistent myocardial motion. although motion compensation methods have been studied throughout the past decade, no clinically accepted solution has emerged. this is partly due to the lack of comprehensive validation. to address this deficit we collected a large multi-centre mr perfusion dataset and used this to characterize typical myocardial motion and confirmed that under clinically relevant conditions motion correction is a frequent requirement (67% of all 586 cases). we then developed a proposed solution which includes both rigid/affine and the non-rigid image registration. quantitative validation has been conducted using 6 different statistics to provide a comprehensive evaluation, showing the proposed techniques to be highly robust to different myocardial anatomy and motion patterns as well as to mr imaging acquisition parameters.
wall motion classification of stress echocardiography based on combined rest-and-stress data. in this paper, we represent a new framework that performs automated local wall motion analysis based on the combined information derived from a rest and stress sequence (a full stress echocardiography study). since cardiac data inherits time-varying and sequential properties, we introduce a hidden markov model (hmm) approach to classify stress echocardiography. a wall segment model is developed for a normal and an abnormal heart and experiments are performed on rest, stress and rest-and-stress sequences. in an assessment using n=44 datasets, combined rest-and-stress analysis shows an improvement in classification (84.17%) over individual rest (73.33%) and stress (68.33%).
automatic deformable diffusion tensor registration for fiber population analysis. in this work, we propose a novel method for deformable tensor---to---tensor registration of diffusion tensor images. our registration method models the distances in between the tensors with geode-sic---loxodromes and employs a version of multi-dimensional scaling (mds) algorithm to unfold the manifold described with this metric. defining the same shape properties as tensors, the vector images obtained through mds are fed into a multi---step vector---image registration scheme and the resulting deformation fields are used to reorient the tensor fields. results on brain dti indicate that the proposed method is very suitable for deformable fiber---to---fiber correspondence and dti---atlas construction.
robotic system for transapical aortic valve replacement with mri guidance. this paper reports our work on developing a robotic surgical system for transapical beating heart aortic valve replacement (avr) under interactive real-time magnetic resonance imaging (rtmri) guidance. our system integrates a real-time mri system, a compound mri robot, as well as an interface for the surgeon to plan the procedure and manipulate the robot. the compound robot consists of a positioning module and a valve delivery module. a 5-dof innomotion positioning arm provides and maintains direct access to the native aortic valve. a newly developed 3-dof robotic valve delivery module allows the surgeon to remotely control bioprosthetic valve delivery with mri guidance. preliminary evaluation of the parameters of the robotic system demonstrates it can provide sufficient capability to successfully assist the surgeon.
bidirectional segmentation of three-dimensional cardiac mr images using a subject-specific dynamical model. statistical model-based segmentation of the left ventricles has received considerable attention these years. while many statistical models have been shown to improve segmentation results, most of them either belong to (1) static models (sm) that neglect the temporal coherence of a cardiac sequence, or (2) generic dynamical models (gdm) that neglect the individual differences of cardiac motion. in this paper, we propose a subject-specific dynamical model (ssdm) that can simultaneously handle inter-subject variability and temporal cardiac dynamics (intra-subject variability). we also design a dynamic prediction algorithm that can progressively predict the shape of a new cardiac sequence at a given frame based on the shapes observed in earlier frames. furthermore, to reduce the accumulation of the segmentation errors throughout the entire sequence, we take into account the periodic nature of cardiac motion and perform bidirectional segmentation from a certain frame in a cardiac sequence. "leave-one-out" validation on 32 sequences show that our algorithm can capture local shape variations and suppress the propagation of segmentation errors.
a tensor-based morphometry study of genetic influences on brain structure using a new fluid registration method. we incorporated a new riemannian fluid registration algorithm into a general mri analysis method called tensor-based morphometry to map the heritability of brain morphology in mr images from 23 monozygotic and 23 dizygotic twin pairs. all 92 3d scans were fluidly registered to a common template. voxelwise jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. lobar volume heritability was also studied using the ace genetic model. the performance of this riemannian algorithm was compared to a more standard fluid registration algorithm: 3d maps from both registration techniques displayed similar heritability patterns throughout the brain. power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. the riemannian algorithm outperformed the standard fluid registration.
assessment of reliability of multi-site neuroimaging via traveling phantom study. this paper describes a framework for quantitative analysis of neuroimaging data of traveling human phantoms used for cross-site validation. we focus on the analysis of magnetic resonance image data including intra- and inter-site comparison. locations and magnitude of geometric deformation is studied via unbiased atlas building and metrics on deformation fields. variability of tissue segmentation is analyzed by comparison of volumes, overlap of tissue maps, and a new kullback-leibler divergence on tissue probabilities, with emphasis on comparing probabilistic rather than binary segmentations. we show that results from this information theoretic measure are highly correlated with overlap. reproducibility of automatic, atlas-based segmentation of subcortical structures is examined by comparison of volumes, shape overlap and surface distances. variability among scanners of the same type but also differences to a different scanner type are discussed. the results demonstrate excellent reliability across multiple sites that can be achieved by the use of the today's scanner generation and powerful automatic analysis software. knowledge about such variability is crucial for study design and power analysis in new multi-site clinical studies.
entropy-optimized texture models. in order to robustly match a statistical model of shape and appearance (e.g. aam) to an unseen image, it is crucial to employ a robust model fittness measure. dense sampling of texture over the region covered by the shape of interest makes comparison of model and image in principle robust. however, when merely texture differences are taken into account, problems with low contrast regions, fuzzy features, global intensity variations, and irregularly varying structures occur.in this paper we introduce a novel entropy-optimized texture model (etm). we map gray values of training images such that pixels represent anatomical structures optimally in terms of information entropy. we match the etm to unseen images employing bayes' law.we validate our approach using four training sets (hearts in basal region, hearts in mid region, brain ventricles, and lumbar vertebrae) and conclude that etms perform better than aams. not only they reduce the average point-to-contour error, they are better suited to cope with large texture variances due to different scanners and even modalities.
preoperative surgery planning for percutaneous hepatic microwave ablation. a novel preoperative surgery planning method is proposed for percutaneous hepatic microwave ablation. an iterative framework for necrosis field simulation and 3d necrosis zone reconstruction is introduced here, and the necrosis model is further superimposed to patient anatomy structures using advanced gpu-accelerated visualization techniques. the full surgery planning is performed by the surgeon in an interactively way, until the optimal surgery plan is achieved. experiments have been performed on realistic patient with hepatic cancer and the actual necrosis zone are measured in postoperative ct images for patient. results show that this method is relative accurate for preoperative trajectory plan and could be used as an assistant to the clinical practice.