waashk/dblp huggingface dataset:
population based analysis of directional information in serial deformation tensor morphometry. deformation morphometry provides a sensitive approach to detecting and mapping subtle volume changes in the brain. population based analyses of this data have been used successfully to detect characteristic changes in different neurodegenerative conditions. however, most studies have been limited to statistical mapping of the scalar volume change at each point in the brain, by evaluating the determinant of the jacobian of the deformation field. in this paper we describe an approach to spatial normalisation and analysis of the full deformation tensor. the approach employs a spatial relocation and reorientation of tensors of each subject. using the assumption of small changes, we use a linear modeling of effects of clinical variables on each deformation tensor component across a population. we illustrate the use of this approach by examining the pattern of significance and orientation of the volume change effects in recovery from alcohol abuse. results show new local structure which was not apparent in the analysis of scalar volume changes.
precise estimation of postoperative cup alignment from single standard x-ray radiograph with gonadal shielding. this paper addresses the problem of estimating postoperative cup alignment from single standard x-ray radiograph with gonadal shielding. the widely used procedure of evaluation of cup orientation following total hip arthroplasty using single standard anteroposterior radiograph is known inaccurate, largely due to the wide variability in individual pelvic position relative to x-ray plate. 2d-3d image registration methods have been introduced to estimate the rigid transformation between a preoperative ct volume and postoperative radiograph(s) for an accurate estimation of the postoperative cup alignment relative to an anatomical reference extracted from the ct data. however, these methods require either multiple radiographs or a radiograph-specific calibration, both of which are not avaiable for most retrospective studies. furthermore, these methods were only evaluated on x-ray radiograph(s) without gonadal shielding. in this paper, we propose to use a hybrid 2d-3d registration scheme combining an iterative landmark-to-ray registration with a 2d-3d intensity-based registration to estimate the rigid transfromation for a precise estimation of cup alignment. quantitative and qualitative results evaluated on clinical and cadaveric datasets are given which indicate the validity of our approach.
deformable density matching for 3d non-rigid registration of shapes. there exists a large body of literature on shape matching and registration in medical image analysis. however, most of the previous work is focused on matching particular sets of features--point-sets, lines, curves and surfaces. in this work, we forsake specific geometric shape representations and instead seek probabilistic representations-- specifically gaussian mixture models--of shapes. we evaluate a closed-form distance between two probabilistic shape representations for the general case where the mixture models differ in variance and the number of components. we then cast non-rigid registration as a deformable density matching problem. in our approach, we take one mixture density onto another by deforming the component centroids via a thin-plate spline (tps) and also minimizing the distance with respect to the variance parameters. we validate our approach on synthetic and 3d arterial tree data and evaluate it on 3d hippocampal shapes.
live-vessel: extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images. this paper incorporates multiscale vesselness filtering into the livewire framework to simultaneously compute optimal medial axes and boundaries in vascular images. to this end, we extend the existing 2d graph search to 3d space to optimize not only for spatial variables (x, y), but also for radius values r at each node. in addition, we minimize change for both scale and the smallest principle curvature and incorporate vessel boundary evidence in our optimization. when compared to two sets of drive expert manual tracings, our proposed technique reduced the overall segmentation task time by 68.2%, had a similarity ratio of 0.772 (0.775 between manual), and was 98.2% reproducible.
towards intra-operative 3d nuclear imaging: reconstruction of 3d radioactive distributions using tracked gamma probes. nuclear medicine imaging modalities assist commonly in surgical guidance given their functional nature. however, when used in the operating room they present limitations. pre-operative tomographic 3d imaging can only serve as a vague guidance intra-operatively, due to movement, deformation and changes in anatomy since the time of imaging, while standard intra-operative nuclear measurements are limited to 1d or (in some cases) 2d images with no depth information. to resolve this problem we propose the synchronized acquisition of position, orientation and readings of gamma probes intra-operatively to reconstruct a 3d activity volume. in contrast to conventional emission tomography, here, in a first proof-of-concept, the reconstruction succeeds without requiring symmetry in the positions and angles of acquisition, which allows greater flexibility. we present our results in phantom experiments for sentinel node lymph node localization. the results indicate that 3d intra-operative nuclear images can be generated in such a setup up to an accuracy equivalent to conventional spect systems. this technology has the potential to advance standard procedures towards intra-operative 3d nuclear imaging and offers a novel approach for robust and precise localization of functional information to facilitate less invasive, image-guided surgery.
a new method for spherical object detection and its application to computer aided detection of pulmonary nodules in ct images. a novel method called local shape controlled voting has been developed for spherical object detection in 3d voxel images. by introducing local shape properties into the voting procedure of normal overlap, the proposed method improves the capability of differentiating spherical objects from other structures, as the normal overlap technique only measures the 'density' of normal overlapping, while how the normals are distributed in 3d is not discovered. the proposed method was applied to computer aided detection of pulmonary nodules based on helical ct images. experiments showed that this method attained a better performance compared to the original normal overlap technique.
using statistical shape analysis for the determination of uterine deformation states during hydrometra. a fundamental prerequisite of hysteroscopy is the proper distension of the uterine cavity with a fluid, also known as hydrometra. for a virtual reality based simulation of hysteroscopy, the uterus deformation process due to different pressure settings has to be modeled. in previous work we have introduced a hybrid method, which relies on precomputed deformation states to derive the hydrometra changes during runtime. however, new offline computations were necessary for every newly introduced organ mesh. this is not viable if a new surgical scene is to be generated for every training session. therefore, we include the deformation states during hydrometra into our previously developed statistical shape model for undeformed organ instances. this allows deriving the hydrometra steps together with new undeformed uterus meshes. these can then be used during the interactive simulation for predicting uterus deformation without time-intensive precomputation steps.
real-time synthesis of image slices in deformed tissue from nominal volume images. this paper presents a fast image synthesis procedure for elastic volumes under deformation. given the node displacements of a mesh and the 3d image voxel data of an undeformed volume, the method maps the image plane pixels to be synthesized from the deformed configuration back to the nominal pre-deformed configuration, where the pixel intensities are obtained easily through interpolation in the regular-grid structure of the voxel volume. for smooth interpolation, this mapping requires the identification of the mesh element enclosing each image pixel. to accelerate this point location procedure, a fast method of marking the image pixels is employed by finding the intersection of the mesh and the image, and marking this intersection on the image pixels using bresenham's line drawing algorithm. a deformable tissue phantom was constructed, it was modeled using the finite element method, and its 3d ultrasound volume was acquired in its undeformed state. actual b-mode images of the phantom under deformation by the ultrasound probe were then compared with the corresponding synthesized images simulated for the same deformations. results show that realistic images can be synthesized in real-time using the proposed technique.
object localization based on markov random fields and symmetry interest points. we present an approach to detect anatomical structures by configurations of interest points, from a single example image. the representation of the configuration is based on markov random fields, and the detection is performed in a single iteration by the max-sum algorithm. instead of sequentially matching pairs of interest points, the method takes the entire set of points, their local descriptors and the spatial configuration into account to find an optimal mapping of modeled object to target image. the image information is captured by symmetry-based interest points and local descriptors derived from gradient vector flow. experimental results are reported for two data-sets showing the applicability to complex medical data.
small animal radiation research platform: imaging, mechanics, control and calibration. in cancer research, well characterized small animal models of human cancer, such as transgenic mice, have greatly accelerated the pace of development of cancer treatments. the goal of the small animal radiation research platform (sarrp) is to make those same models available for the development and evaluation of novel radiation therapies. in combination with advanced imaging methods, small animal research allows detailed study of biological processes, disease progression, and response to therapy, with the potential to provide a natural bridge to the clinical environment. the sarrp will realistically model human radiation treatment methods in standard animal models. in this paper, we describe the mechanical and control structure of the system. this system requires accurate calibration of the x-ray beam for both imaging and radiation treatment, which is presented in detail in the paper.
alignment of large image series using cubic b-splines tessellation: application to transmission electron microscopy data. 3d reconstruction from serial 2d microscopy images depends on non-linear alignment of serial sections. for some structures, such as the neuronal circuitry of the brain, very large images at very high resolution are necessary to permit reconstruction. these very large images prevent the direct use of classical registration methods. we propose in this work a method to deal with the non-linear alignment of arbitrarily large 2d images using the finite support properties of cubic b-splines. after initial affine alignment, each large image is split into a grid of smaller overlapping sub-images, which are individually registered using cubic b-splines transformations. inside the overlapping regions between neighboring sub-images, the coefficients of the knots controlling the b-splines deformations are blended, to create a virtual large grid of knots for the whole image. the sub-images are resampled individually, using the new coefficients, and assembled together into a final large aligned image. we evaluated the method on a series of large transmission electron microscopy images and our results indicate significant improvements compared to both manual and affine alignment.
probabilistic fiber tracking using particle filtering. this paper presents a novel and fast probabilistic method for white matter fiber tracking from diffusion weighted mri (dwi). we formulate fiber tracking on a nonlinear state space model which is able to capture both smoothness regularity of fibers and uncertainties of the local fiber orientations due to noise and partial volume effects. the global tracking model is implemented using particle filtering, which allows us to recursively compute the posterior distribution of the potential fibers. the fiber orientation distribution is theoretically formulated for prolate and oblate tensors separately. fast and efficient sampling is realised using the von mises-fisher distribution on unit spheres. given a seed point, the method is able to rapidly locate the global optimal fiber and also provide a connectivity map. the proposed method is demonstrated on a brain dataset.
signal lmmse estimation from multiple samples in mri and dt-mri. a method to estimate the magnitude mr data from several noisy samples is presented. it is based on the linear minimum mean squared error (lmmse) estimator for the rician noise model when several scanning repetitions are available. this method gives a closed-form analytical solution that takes into account the probability distribution of the data as well as the existing level of noise, showing a better performance than methods such as the average or the median.
prior knowledge driven multiscale segmentation of brain mri. we present a novel automatic multiscale algorithm applied to segmentation of anatomical structures in brain mri. the algorithm which is derived from algebraic multigrid, uses a graph representation of the image and performs a coarsening process that produces a full hierarchy of segments. our main contribution is the incorporation of prior knowledge information into the multiscale framework through a bayesian formulation. the probabilistic information is based on an atlas prior and on a likelihood function estimated from a manually labeled training set. the significance of our new approach is that the constructed pyramid, reflects the prior knowledge formulated. this leads to an accurate and efficient methodology for detection of various anatomical structures simultaneously. quantitative validation results on gold standard mri show the benefit of our approach.
a study of hippocampal shape difference between genders by efficient hypothesis test and discriminative deformation. hypothesis testing is an important way to detect the statistical difference between two populations. in this paper, we use the fisher permutation and bootstrap tests to differentiate hippocampal shape between genders. these methods are preferred to traditional hypothesis tests which impose assumptions on the distribution of the samples. an efficient algorithm is adopted to rapidly perform the exact tests. we extend this algorithm to multivariate data by projecting the original data onto an "informative direction" to generate a scalar test statistic. this "informative direction" is found to preserve the original discriminative information. this direction is further used in this paper to isolate the discriminative shape difference between classes from the individual variability, achieving a visualization of shape discrepancy.
spatiotemporal normalization for longitudinal analysis of gray matter atrophy in frontotemporal dementia. we present a unified method, based on symmetric diffeomorphisms, for studying longitudinal neurodegeneration. our method first uses symmetric diffeomorphic normalization to find a spatiotemporal parameterization of an individual's image time series. the second step involves mapping a representative image or set of images from the time series into an optimal template space. the template mapping is then combined with the intrasubject spatiotemporal map to enable pairwise statistical tests to be performed on a population of normalized time series images. here, we apply this longitudinal analysis protocol to study the gray matter atrophy patterns induced by frontotemporal dementia (ftd). we sample our normalized spatiotemporal maps at baseline (time zero) and time one year to generate an annualized atrophy map (aam) that estimates the annual effect of ftd. this spatiotemporal normalization enables us to locate neuroanatomical regions that consistently undergo significant annual gray matter atrophy across the population. we found the majority of annual atrophy to occur in the frontal and temporal lobes in our population of 20 subjects. we also found significant effects in the hippocampus, insula and cingulate gyrus. our novel results, significant at p < 0.05 after false discovery rate correction, are represented in local template space but also assigned talairach coordinates and brodmann and anatomical automatic labeling (aal) labels. this paper shows the statistical power of symmetric diffeomorphic normalization for performing deformation-based studies of longitudinal atrophy.
multi-criteria trajectory planning for hepatic radiofrequency ablation. in this paper, we propose a method based on multiple criteria to assist physicians in planning percutaneous rfa on liver. we explain how we extracted information from literature and interviews with radiologists, and formalized them into geometric constraints. we expose then our method to compute the most suitable needle insertion in two steps: computation of authorized insertion zones and multi-criteria optimization of the trajectory within this zones. we focus on the combination of the criteria to optimize and on the optimization step.
effectiveness of the finite impulse response model in content-based fmri image retrieval. the thresholded t-map produced by the general linear model (glm) gives an effective summary of activation patterns in functional brain images and is widely used for feature selection in fmri related classification tasks. as part of a project to build content-based retrieval systems for fmri images, we have investigated ways to make glm more adaptive and more robust in dealing with fmri data from widely differing experiments. in this paper we report on exploration of the finite impulse response model, combined with multiple linear regression, to identify the "locally best hemodynamic response function (hrf) for each voxel" and to simultaneously estimate activation levels corresponding to several stimulus conditions. the goal is to develop a procedure for processing datasets of varying natures. our experiments show that finite impulse response (fir) models with a smoothing factor produce better retrieval performance than does the canonical double gamma hrf in terms of retrieval accuracy.
customised cytoarchitectonic probability maps using deformable registration: primary auditory cortex. a novel method is presented for creating a probability map from histologically defined cytoarchitectonic data, customised for the anatomy of individual fmri volunteers. postmortem structural and cytoarchitectonic information from a published dataset is combined with high resolution structural mr images using deformable registration of a region of interest. in this paper, we have targeted the three subareas of the primary auditory cortex (located on heschl's gyrus); however, the method could be applied to any other cytoarchitectonic region. the resulting probability maps show a significantly higher overlap than previously generated maps using the same cytoarchitectonic data, and more accurately span the macroanatomical structure of the auditory cortex. this improvement indicates a high potential for spatially accurate fmri analysis, allowing more reliable correlation between anatomical structure and function. we validate the approach using fmri data from nine individuals, taken from a published dataset. we compare activation for stimuli evoking a pitch percept to activation for acoustically matched noise, and demonstrate that the primary auditory cortex (te1.0) and the lateral region te1.2 are sensitive to pitch, whereas te1.1 is not.
towards 3d ultrasound image based soft tissue tracking: a transrectal ultrasound prostate image alignment system. the emergence of real-time 3d ultrasound (us) makes it possible to consider image-based tracking of subcutaneous soft tissue targets for computer guided diagnosis and therapy. we propose a 3d transrectal us based tracking system for precise prostate biopsy sample localisation. the aim is to improve sample distribution, to enable targeting of unsampled regions for repeated biopsies, and to make post-interventional quality controls possible. since the patient is not immobilized, since the prostate is mobile and due to the fact that probe movements are only constrained by the rectum during biopsy acquisition, the tracking system must be able to estimate rigid transformations that are beyond the capture range of common image similarity measures. we propose a fast and robust multiresolution attribute-vector registration approach that combines global and local optimization methods to solve this problem. global optimization is performed on a probe movement model that reduces the dimensionality of the search space and thus renders optimization efficient. the method was tested on 237 prostate volumes acquired from 14 different patients for 3d to 3d and 3d to orthogonal 2d slices registration. the 3d-3d version of the algorithm converged correctly in 96.7% of all cases in 6.5s with an accuracy of 1.41mm (r.m.s.) and 3.84mm (max). the 3d to slices method yielded a success rate of 88.9% in 2.3s with an accuracy of 1.37mm (r.m.s.) and 4.3mm (max).
accuracy assessment of global and local atrophy measurement techniques with realistic simulated longitudinal data. the main goal of this work was to assess the accuracy of several well-known methods which provide global (bsi and siena) or local (jacobian integration) estimates of longitudinal atrophy in brain structures using magnetic resonance images. for that purpose, we have generated realistic simulated images which mimic the patterns of change obtained from a cohort of 19 real controls and 27 probable alzheimer's disease patients. siena and bsi results correlate very well with gold standard data (bsi mean absolute error < 0.29%; siena < 0.44%). jacobian integration was guided by both fluid and ffd-based registration techniques and resulting deformation fields and associated jacobians were compared, region by region, with gold standard ones. the ffd registration technique provided more satisfactory results than the fluid one. mean absolute error differences between volume changes given by the ffd-based technique and the gold standard were: sulcal csf < 2.49%; lateral ventricles < 2.25%; brain < 0.36%; hippocampi < 1.42%.
shape analysis with overcomplete spherical wavelets. in this paper, we explore the use of over-complete spherical wavelets in shape analysis of closed 2d surfaces. previous work has demonstrated, theoretically and practically, the advantages of over-complete over bi-orthogonal spherical wavelets. here we present a detailed formulation of over-complete wavelets, as well as shape analysis experiments of cortical folding development using them. our experiments verify in a quantitative fashion existing qualitative theories of neuro-anatomical development. furthermore, the experiments reveal novel insights into neuro-anatomical development not previously documented.