waashk/dblp huggingface dataset:
a method to correct for brain shift when building electrophysiological atlases for deep brain stimulation (dbs) surgery. to help surgeons to pre-operatively select the target location for dbs electrodes, functional atlases based on intra-operatively acquired data have been created in the past. recently, many groups have reported on the occurrence of brain shift in stereotactic surgery and its impact on the procedure but not on the creation of such atlases. due to brain shift, the pre- and intra-operative coordinates of anatomic structures are different. when building large population atlases, which rely on pre-operative images for normalization purposes, it is thus necessary to correct for this difference. in this paper, we propose a method to achieve this. we show evidence that electrophysiological maps built using corrected and uncorrected data are different and that the maps created using shift-corrected data correlate better than those created using uncorrected data with the final position of the implant. these findings suggest that brain-shift correction of intra-operatively recorded data is feasible for the construction of accurate shift-corrected electrophysiological atlases.
expertise modeling for automated planning of acetabular cup in total hip arthroplasty using combined bone and implant statistical atlases. intraoperative robotic and computer-guided assistances are now commonly used in total hip arthroplasty (tha) for accurate execution of the preoperative plan. although the preoperative plan to be accurately executed is critical, it is still interactively prepared in a time-consuming and subjective manner. in this paper, atlas-based approach to automated surgical planning of the acetabular cup in tha is described to stabilize its quality as well as reduce its time-consuming nature. surgeon's expertise is embedded in two types of statistical atlases, which are constructed from training datasets of ct-based 3d plans prepared by experienced surgeons. one is a statistical shape model which encodes global spatial relationships between the patient anatomy and implant. the other is the statistical map of residual bone thickness on the implant surface, which encodes local spatial constraints of the anatomy and implant. given the 3d pelvis shape of the patient, we formulate a procedure to determine the best size and position of the acetabular cup which satisfy the constraints derived from the two statistical atlases. we validated the proposed planning method by retrospective study using the datasets which were actually used in the tha surgery.
a novel intensity similarity metric with soft spatial constraint for a deformable image registration problem in radiation therapy. in this paper we propose a novel similarity metric and a method for deformable registration of two images for a specific clinical application. the basic assumption in almost all deformable registration approaches is that there exist explicit correspondences between pixels across the two images. this principle is used to design image (dis)similarity metrics, such as sum of squared differences (ssd) or mutual information (mi). this assumption is strongly violated, for instance, within specific regions of images from abdominal or pelvic section of a patient taken at two different time points. nevertheless, in some clinical applications, it is required to compute a smooth deformation field for all the regions within the image including the boundaries of such regions. in this paper, we propose a deformable registration method, which utilizes a priori intensity distributions of the regions delineated on one of the images to devise a new similarity measure that varies across regions of the image to establish a smooth and robust deformation field. we present validation results of the proposed method in mapping bladder, prostate, and rectum contours of computer tomography (ct) volumes of 10 patients taken for prostate cancer radiotherapy treatment planning and verification.
parcellation of fmri datasets with ica and pls-a data driven approach. inter-subject parcellation of functional magnetic resonance imaging (fmri) data based on a standard general linear model (glm) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fmri. however, for all its appeal, a glm-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of hemodynamic response function (hrf) and task-related signal changes, or about the subject behaviour during the task.in this paper, we introduce a data-driven version of the spectral clustering parcellation, based on independent component analysis (ica) and partial least squares (pls) instead of the glm. first, a number of independent components are automatically selected. seed voxels are then obtained from the associated ica maps and we compute the pls latent variables between the fmri signal of the seed voxels (which covers regional variations of the hrf) and the principal components of the signal across all voxels. finally, we parcellate all subjects data with a spectral clustering of the pls latent variables.we present results of the application of the proposed method on both single-subject and multi-subject fmri datasets. preliminary experimental results, evaluated with intra-parcel variance of glm t-values and pls derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over glm based techniques.
correcting motion artifacts in retinal spectral domain optical coherence tomography via image registration. spectral domain optical coherence tomography (sd-oct) is an important tool for the diagnosis of various retinal diseases. the measurements available from sd-oct volumes can be used to detect structural changes in glaucoma patients before the resulting vision loss becomes noticeable. eye movement during the imaging process corrupts the data, making measurements unreliable. we propose a method to correct for transverse motion artifacts in sd-oct volumes after scan acquisition by registering the volume to an instantaneous, and therefore artifact-free, reference image. our procedure corrects for smooth deformations resulting from ocular tremor and drift as well as the abrupt discontinuities in vessels resulting from microsaccades. we test our performance on 48 scans of healthy eyes and 116 scans of glaucomatous eyes, improving scan quality in 96% of healthy and 73% of glaucomatous eyes.
a fast alternative to computational fluid dynamics for high quality imaging of blood flow. obtaining detailed, patient-specific blood flow information would be very useful in detecting and monitoring cardio-vascular diseases. current approaches rely on computational fluid dynamics to achieve this; however, these are hardly usable in the daily clinical routine due to the required technical supervision and long computing times. we propose a fast measurement enhancement method that requires neither supervision nor long computation and it is the objective of this paper to evaluate its performance as compared to the state-of-the-art. to this end a large set of abdominal aortic bifurcation geometries was used to test this technique and the results were compared to measurements and numerical simulations. we find that this method is able to dramatically improve the quality of the measurement information, in particular the flow-derived quantities such as wall shear stress. additionally, good estimation of unmeasurable quantities such as pressure can be provided. we demonstrate that this approach is a practical and clinically feasible alternative to fully-blown, time-consuming, patient-specific flow simulations.
modeling adaptation effects in fmri analysis. the standard general linear model (glm) for rapid event-related fmri design protocols typically ignores reduction in hemodynamic responses in successive stimuli in a train due to incomplete recovery from the preceding stimuli. to capture this adaptation effect, we incorporate a region-specific adaptation model into glm. the model quantifies the rate of adaptation across brain regions, which is of interest in neuroscience. empirical evaluation of the proposed model demonstrates its potential to improve detection sensitivity. in the fmri experiments using visual and auditory stimuli, we observed that the adaptation effect is significantly stronger in the visual area than in the auditory area, suggesting that we must account for this effect to avoid bias in fmri detection.
two-tensor tractography using a constrained filter. we describe a technique to simultaneously estimate a weighted, positive-definite multi-tensor fiber model and perform tractography. existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. we formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by the previous. to do this we model the signal as a weighted mixture of gaussian tensors and perform tractography within a filter framework. starting from a seed point, each fiber is traced to its termination using an unscented kalman filter to simultaneously fit the local model and propagate in the most consistent direction. further, we modify the kalman filter to enforce model constraints, i.e. positive eigenvalues and convex weights. despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. synthetic experiments demonstrate that this approach significantly improves the angular resolution at crossings and branchings while consistently estimating the mixture weights. in vivo experiments confirm the ability to trace out fibers in areas known to contain such crossing and branching while providing inherent path regularization.
iterative co-linearity filtering and parameterization of fiber tracts in the entire cingulum. we present a method for the fully automated extraction of the cingulum using diffusion tensor imaging (dti) data. we perform whole-brain tractography and initialize tract selection in the cingulum with a registered dti atlas. tracts are parameterized from which tract co-linearity is derived. the tract set, filtered on the basis of co-linearity with the cingulum shape, yields an improved segmentation of the cingulum and is subsequently optimized in an iterative fashion to further improve the tract selection. we evaluate the method using a large dti database of 500 subjects from the general population and show robust extraction of tracts in the entire cingulate bundle in both hemispheres. we demonstrate the use of the extracted fiber-tracts to compare left and right cingulate bundles. our asymmetry analysis shows a higher fractional anisotropy in the left anterior part of the cingulum compared to the right side, and the opposite effect in the posterior part.
nibart: a new interval based algebraic reconstruction technique for error quantification of emission tomography images. this article presents a new algebraic method for reconstructing emission tomography images. this approach is mostly an interval extension of the conventional sirt algorithm. one of the main characteristic of our approach is that the reconstructed activity associated with each pixel of the reconstructed image is an interval whose length can be considered as an estimate of the impact of the random variation of the measured activity on the reconstructed image. this work aims at investigating a new methodological concept for a reliable and robust quantification of reconstructed activities in scintigraphic images.
dual tensor atlas generation based on a cohort of coregistered non-hardi datasets. we propose a method to create a dual tensor atlas from multiple coregistered non-hardi datasets. increased angular resolution is ensured by random variations of subject positioning in the scanner and different local rotations applied during coregistration resulting in dispersed gradient directions. simulations incorporating residual coregistration misalignments show that using 10 subjects should already double the angular resolution, even at a relatively low b-value of b = 1000 smm¿ 2. commisural corpus callosum fibers reconstructed by our method closely approximated those found in a hardi dataset.
system design of a hand-held mobile robot for craniotomy. this contribution reports the development and initial testing of a mobile robot system for surgical craniotomy, the craniostar. a kinematic system based on a unicycle robot is analysed to provide local positioning through two spiked wheels gripping directly onto a patients skull. a control system based on a shared control system between both the surgeon and robot is employed in a hand-held design that is tested initially on plastic phantom and swine skulls. results indicate that the system has substantially lower risk than present robotically assisted craniotomies, and despite being a hand-held mobile robot, the craniostar is still capable of sub-millimetre accuracy in tracking along a trajectory and thus achieving an accurate transfer of pre-surgical plan to the operating room procedure, without the large impact of current medical robots based on modified industrial robots.
-brush: a gaze-contingent virtual paintbrush for dense 3d reconstruction in robotic assisted surgery. with increasing demand on intra-operative navigation and motion compensation during robotic assisted minimally invasive surgery, real-time 3d deformation recovery remains a central problem. currently the majority of existing methods rely on salient features, where the inherent paucity of distinctive landmarks implies either a semi-dense reconstruction or the use of strong geometrical constraints. in this study, we propose a gaze-contingent depth reconstruction scheme by integrating human perception with semi-dense stereo and p-q based shading information. depth inference is carried out in real-time through a novel application of bayesian chains without smoothness priors. the practical value of the scheme is highlighted by detailed validation using a beating heart phantom model with known geometry to verify the performance of gaze-contingent 3d surface reconstruction and deformation recovery.
hybrid spline-based multimodal registration using local measures for joint entropy and mutual information. we introduce a new hybrid approach for spline-based elastic registration of multimodal medical images. the approach uses point landmarks as well as intensity information based on local analytic measures for joint entropy and mutual information. the information-theoretic similarity measures are computationally efficient and can be optimized independently for each voxel. we have applied our approach to synthetic images, brain phantom images, as well as clinically relevant multimodal medical images. we also compared our measures with previous measures.
biopsy site re-localisation based on the computation of epipolar lines from two previous endoscopic images. tracking biopsy sites in endoscopic images can be useful to provide a visual aid for the guidance of surgical tools, for example when endoscopic guided biopsy is required. a new method for re-localisation of these sites is presented in this paper. it makes use of epipolar geometry properties between three images of the same site observed from different viewpoints with an endoscope. two epipolar lines are derived from the two first images in the third image where the site needs to be re-localised. their intersection corresponds to the location of the biopsy site. this method was tested with gastroscopic data from 2 patients with 9 series of three images of the oesophagus. the re-localisation error was estimated at less than 1.5 millimetres by a clinical endoscopist, which is sufficient for most clinical endoscopic applications.
multivariate tensor-based brain anatomical surface morphometry via holomorphic one-forms. here we introduce multivariate tensor-based surface morphometry using holomorphic one-forms to study brain anatomy. we computed new statistics from the riemannian metric tensors that retain the full information in the deformation tensor fields. we introduce two different holomorphic one-forms that induce different surface conformal parameterizations. we applied this framework to 3d mri data to analyze hippocampal surface morphometry in alzheimer's disease (ad; 26 subjects), lateral ventricular surface morphometry in hiv/aids (19 subjects) and cortical surface morphometry in williams syndrome (ws; 80 subjects). experimental results demonstrated that our method powerfully detected brain surface abnormalities. multivariate statistics on the local tensors outperformed other tbm methods including analysis of the jacobian determinant, the largest eigenvalue, or the pair of eigenvalues, of the surface jacobian matrix.
attribute vector guided groupwise registration. groupwise registration has been recently introduced for simultaneous registration of a group of images with the goal of constructing an unbiased atlas. to this end, direct application of information-theoretic entropy measures on image intensity has achieved various successes. however, simplistic voxelwise utilization of image intensity often neglects important contextual information, which can be provided by more comprehensive geometric and statistical features. in this paper, we employ attribute vectors, instead of image intensities, to guide groupwise registration. in particular, for each voxel, the attribute vector is computed from its multiple-scale neighborhoods to capture geometric information at different scales. moreover, the probability density function (pdf) of each attribute in the vector is then estimated from the local neighborhood, providing a statistical summary of the underlying anatomical structure. for the purpose of registration, jensen-shannon (js) divergence is used to measure the pdf dissimilarity of each attribute at corresponding locations of different individual images. by minimizing the overall js divergence in the whole image space and estimating the deformation field of each image simultaneously, we can eventually register all images and build an unbiased atlas. experimental results indicate that our method yields better registration quality, compared with a popular groupwise registration method.
local white matter geometry indices from diffusion tensor gradients. we introduce a framework for computing geometrical properties of white matter fibres directly from diffusion tensor fields. the key idea is to isolate the portion of the gradient of the tensor field corresponding to local variation in tensor orientation, and to project it onto a coordinate frame of tensor eigenvectors. the resulting eigenframe-centered representation makes it possible to define scalar geometrical measures that describe the underlying white matter fibres, directly from the diffusion tensor field and its gradient, without requiring prior tractography. we define two new scalar measures of (1) fibre dispersion and (2) fibre curving, and we demonstrate them on synthetic and in-vivo datasets. finally, we illustrate their applicability in a group study on schizophrenia.
a general pde-framework for registration of contrast enhanced images. this paper presents a general pde-framework for registration of contrast enhanced images. the approach directly applies the idea of separating the contrast enhancement term from the images in the regularization terms. in our formulation, we stay consistent with existing non-parametric image registration techniques, however, we carry an additional contrast enhancement term throughout. a mathematically rigorous approach is pursued which can exploit various forms of regularization. in this paper, our experiments are built based on diffusion regularization for both contrast enhancement and the deformation field.
robust extrapolation scheme for fast estimation of 3d ising field partition functions: application to within-subject fmri data analysis. in this paper, we present a fast numerical scheme to estimate partition functions (pf) of 3d ising fields. our strategy is applied to the context of the joint detection-estimation of brain activity from functional magnetic resonance imaging (fmri) data, where the goal is to automatically recover activated regions and estimate region-dependent hemodynamic filters. for any region, a specific binary markov random field may embody spatial correlation over the hidden states of the voxels by modeling whether they are activated or not. to make this spatial regularization fully adaptive, our approach is first based upon a classical path-sampling method to approximate a small subset of reference pfs corresponding to prespecified regions. then, the proposed extrapolation method allows us to approximate the pfs associated with the ising fields defined over the remaining brain regions. in comparison with preexisting approaches, our method is robust to topological inhomogeneities in the definition of the reference regions. as a result, it strongly alleviates the computational burden and makes spatially adaptive regularization of whole brain fmri datasets feasible.
slipping objects in image registration: improved motion field estimation with direction-dependent regularization. the computation of accurate motion fields is a crucial aspect in 4d medical imaging. it is usually done using a non-linear registration without further modeling of physiological motion properties. however, a globally homogeneous smoothing (regularization) of the motion field during the registration process can contradict the characteristics of motion dynamics. this is particularly the case when two organs slip along each other which leads to discontinuities in the motion field. in this paper, we present a diffusion-based model for incorporating physiological knowledge in image registration. by decoupling normal- and tangential-directed smoothing, we are able to estimate slipping motion at the organ borders while ensuring smooth motion fields in the inside and preventing gaps to arise in the field. we evaluate our model focusing on the estimation of respiratory lung motion. by accounting for the discontinuous motion of visceral and parietal pleurae, we are able to show a significant increase of registration accuracy with respect to the target registration error (tre).
evaluation of 4d-ct lung registration. non-rigid registration accuracy assessment is typically performed by evaluating the target registration error at manually placed landmarks. for 4d-ct lung data, we compare two sets of landmark distributions: a smaller set primarily defined on vessel bifurcations as commonly described in the literature and a larger set being well-distributed throughout the lung volume. for six different registration schemes (three in-house schemes and three schemes frequently used by the community) the landmark error is evaluated and found to depend significantly on the distribution of the landmarks. in particular, lung regions near to the pleura show a target registration error three times larger than near-mediastinal regions. while the inter-method variability on the landmark positions is rather small, the methods show discriminating differences with respect to consistency and local volume change. in conclusion, both a well-distributed set of landmarks and a deformation vector field analysis are necessary for reliable non-rigid registration accuracy assessment.