nils-herrmann/clean_scirepeval huggingface dataset:
TruHARP: single breath-hold MRI for high resolution cardiac motion and strain quantification

MRI techniques for tissue motion and strain quantifications have limited resolution because of interference from the conjugate echo or spectral peak in Fourier space. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to long image acquisition time. In this paper, we propose TruHARP, an MRI motion and strain quantification methodology that involves a novel single breath-hold imaging protocol. In post-processing, TruHARP separates the spectral peaks in the acquired datasets, enabling high resolution motion and strain quantification. The impact of high resolution on circumferential and radial strain is studied using a realistic simulation and the improvement in strain maps is demonstrated in an in-vivo human study.
A projection space map method for limited angle reconstruction

Presents a method to reconstruct images from finite sets of noisy projections which are available only over limited or sparse angles. The method solves a constrained optimization problem to find a maximum a posteriori (MAP) estimate of the full 2-D Radon transform of the object, using prior knowledge of object mass, center of mass, and convex support, and information about fundamental constraints and smoothness of the Radon transform. This efficient primal-dual algorithm consists of an iterative local relaxation stage which solves a partial differential equation in Radon-space, followed by a simple Lagrange multiplier update stage. The object is reconstructed using convolution backprojection applied to the Radon transform estimate.<<ETX>>
Fast block-based motion estimation via DC matching

This paper introduces a fast block-based motion estimation algorithm based on matching DC components. The idea is simple: blocks cannot match well if their corresponding DC components do not match well. We can take advantage of this observation to translate the expensive 2D block matching problem to a simpler 1D matching one by quickly eliminating a majority of matching candidates. Our novel motion estimation algorithm offers computational scalability through a single parameter and the global optimum can still be achieved. Moreover, an efficient implementation of computing DCs and buffering recyclable data is also presented. Experiments show that the proposed algorithm is several times faster than the exhaustive search algorithm with nearly identical prediction performance.
Topology preserving automatic segmentation of the spinal cord in magnetic resonance images

Magnetic resonance images of the spinal cord play an important role in studying neurological diseases, particularly multiple sclerosis, where spinal cord atrophy can provide a measure of disease progression and disability. Current practices involve segmenting the spinal cord manually, which can be an inconsistent and time-consuming process. We present an automatic segmentation method for the spinal cord using a novel combination of deformable atlas based registration and topology preserving classification to address the challenges inherent to MR images of the spinal cord. Using real MR data, our method is shown to be highly accurate when compared to segmentations by manual raters. In addition, our results always maintain the correct topology of the spinal cord, therefore providing segmentations more consistent with the known anatomy.
A Rician mixture model classification algorithm for magnetic resonance images

Tissue classification algorithms developed for magnetic resonance images commonly assume a Gaussian model on the statistics of noise in the image. While this is approximately true for voxels having large intensities, it is less true as the underlying intensity becomes smaller. In this paper, the Gaussian model is replaced with a Rician model, which is a better approximation to the observed signal. A new classification algorithm based on a finite mixture model of Rician signals is presented wherein the expectation maximization algorithm is used to find the joint maximum likelihood estimates of the unknown mixture parameters. Improved accuracy of tissue classification is demonstrated on several sample data sets. It is also shown that classification repeatability for the same subject under different MR acquisitions is improved using the new method.
Representation and transformation of uncertainty in an evidence theory framework

Interpreting uncertain information, a fundamental requirement of many computer vision and pattern recognition systems is commonly supported by models of the uncertainty. Evidence theory, also called Dempster-Shafer theory, is particularly useful for representing and combining uncertain information when a single precise uncertainty model is unavailable. A framework is presented for deriving and transforming evidence-theoretic belief representations of uncertain variables that denote numerical quantities. Belief is derived from probabilistic models using relationships between probability bounds and the support and plausibility functions used in evidence theory. This model-based approach to belief representation is illustrated by an algorithm currently used in a vision system to label anomalous high-intensity pixels in imagery. As the uncertain variables are manipulated to form features and object discriminants, the belief representation of the uncertain variables must be transformed accordingly. Belief transformations, analogous to the transformation of probability-density functions in mappings of random variables, are derived to maintain the same rigorous belief representation for computed quantities. The results demonstrate novel ways to address uncertainty in the use of sensor information, and contribute to understanding of the similarities and distinctions of probability theory and evidence theory.<<ETX>>
Atlas-assisted tomography: registration of a deformable atlas to compensate for limited-angle cone-beam trajectory

We present a method to improve the quality of cone-beam tomographic images computed from an intra-operative C-arm scan by adding information from an anatomical atlas. Limited range of C-arm view angles leads to reconstruction artifacts and poor anatomical detail. We propose to complete the missing views with simulated projections of a statistical anatomical model, which is deformably registered to match the data in the C-arm images. This paper presents the methods used to create the atlas and to register it with X-ray images. We compare the results of seven leave-one-out simulated hybrid reconstruction tests on a population of 13 subjects, with "ground-truth" CT, a classical short-scan, and a partial-scan reconstruction
Topology-preserving STAPLE

Methodology for fusing multiple segmentations to produce an improved result has been useful in computational anatomical studies. Although obtaining segmentations of anatomy having a particular topology are essential to studies using diffeomorphic deformation based analyses, no methods of label fusion presented to date have incorporated information regarding the topology of the anatomy. In this paper, we introduce “Topology STAPLE”, a novel method that statistically fuses multiple rater segmentations into a topologically correct segmentation. We evaluate the method on both simulated data and real delineations of the cerebellum produced by human raters.
An Eulerian PDE approach for computing tissue thickness

We outline an Eulerian framework for computing the thickness of tissues between two simply connected boundaries that does not require landmark points or parameterizations of either boundary. Thickness is defined as the length of correspondence trajectories, which run from one tissue boundary to the other, and which follow a smooth vector field constructed in the region between the boundaries. A pair of partial differential equations (PDEs) that are guided by this vector field are then solved over this region, and the sum of their solutions yields the thickness of the tissue region. Unlike other approaches, this approach does not require explicit construction of any correspondence trajectories. An efficient, stable, and computationally fast solution to these PDEs is found by careful selection of finite differences according to an up-winding condition. The behavior and performance of our method is demonstrated on two simulations and two magnetic resonance imaging data sets in two and three dimensions. These experiments reveal very good performance and show strong potential for application in tissue thickness visualization and quantification.
CTREC: C-arm tracking and reconstruction using elliptic curves

C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct three-dimensional information, attributable to the difficulty in obtaining the pose of X-ray images in 3D space. We propose a unified mathematical framework to address the issues of intra-operative pose estimation, correspondence and reconstruction, using simple elliptic curves. In contrast to other fiducial-based tracking methods, our method uses a single ellipse to constrain 5 out of 6 degrees of freedom of C-arm pose, along with randomly distributed unknown points in the imaging volume (either naturally present or induced by randomly placed beads or other markers in the image space) from two images/views to completely recover the Carm pose. Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0.51. and 0.12. STD. The method appears to be sufficiently accurate and appealing for many clinical applications, since it uses a simple elliptic fiducial coupled with patient information and has very minimal interference with the workspace.
Contour estimation in tagged cardiac magnetic resonance images

Tracking magnetic resonance tags in myocardial tissue promises to be an effective tool in the assessment of myocardial motion and deformation . The amount of data acquired is very large, requiring automated tracking methods. We describe a hierarchy of image processing steps that quickly detect the contours of the myocardial boundaries of the left ventricle (LV) and the spines of tags placed in the myocardium. We are currently using our technique to process short axis (SA) images containing a radial tag pattern and long axis (LA) images containing a parallel tag pattern. Other tag patterns, such as striped tags or a rectangular grid, may be detected with minor modifications to the algorithm.
Imaging the evolution of three-dimensional myocardial strains using a fast MR imaging technique

In this paper we describe a fast magnetic resonance imaging (MRI) technique that can be used to measure the evolution of three-dimensional myocardial strains in multiple stacks of left-ventricular short axis (SA) slices. The SA slices are selectively tagged with planes of magnetic saturation. The MR data is then acquired using a fast imaging technique that combines the principles of previously demonstrated harmonic phase (HARP) and strain encoded (SENC) imaging methodologies to measure the in-plane circumferential and radial strains, and the out-of-plane longitudinal strains in two short breath-holds. Strain measurements in eight regions at representative time frames in five SA slices in a normal volunteer are presented.
Probabilistic fiber tracking using a modified Lasso bootstrap method

Diffusion MRI (dMRI) provides a noninvasive tool for investigating white matter tracts. Probabilistic fiber tracking has been proposed to represent the fiber structures as 3D streamlines while taking the uncertainty introduced by noise into account. In this paper, we propose a probabilistic fiber tracking method based on bootstrapping a multi-tensor model with a fixed tensor basis. The fiber orientation (FO) estimation is formulated as a Lasso problem. Then by resampling the residuals calculated using a modified Lasso estimator to create synthetic diffusion signals, a distribution of FOs is estimated. Probabilistic fiber tracking can then be performed by sampling from the FO distribution. Experiments were performed on a digital crossing phantom and brain dMRI for validation.
Gradient vector flow: a new external force for snakes

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to concave boundaries, however, have limited their utility. This paper develops a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF) is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The resultant field has a large capture range and forces active contours into concave regions. Examples on simulated images and one real image are presented.
Automatically identifying white matter tracts using cortical labels

Diffusion tensor imaging (DTI) has become a standard clinical procedure in assessing the health of white matter in the brain. Tractography, the tracing of individual fibers in the brain using DTI data, has begun to play a more central role in neuroscience research, particularly in understanding the relationships between brain connectivity and behavior. The measuring of features related to bundles of fibers, i.e., tracts or fasciculi, is currently problematic because of the need for manual interaction. This article presents an algorithm for the automatic identification of selected white matter tracts. It extracts fibers using the FACT algorithm and finds cortical gy- ral labels using a multi-atlas deformable registration scheme. Tracts are identified as the fibers passing between selected cortical labels. The quality of automatic labels are compared both visually and numerically against a well-accepted manual approach. The automatic approach is shown to be more consistent with conventional definitions of tracts and more re- peatable on separate scans of the same subject.
The kriging update model and recursive space-time function estimation

We present a method for efficiently fitting a time series of spatial functions to observed data. The method is closely related to kriging, which is an interpolation method based on a stochastic data model. While kriging is effective and versatile for estimating individual functions from observed data, it must be extended to incorporate temporal correlation. In this paper, we introduce temporal correlation to kriging in the form of a stochastic state equation representation-the kriging update model. This permits a recursive solution that is akin to Kalman filtering to estimate time series of functions that avoids growing data problems associated with other space-time extensions of kriging. The state equation representation incorporates the principle assumption of universal kriging: that the mean is deterministic but unknown. We derive the estimate using best linear unbiased estimation and state the result in a concise algorithm for general use in arbitrary spatial dimensions. To demonstrate the algorithm, we apply it to two sets of test functions and provide an example application in estimating heart motion from medical images.
Multi-layer fast level set segmentation for macular OCT

Segmenting optical coherence tomography (OCT) images of the retina is important in the diagnosis, staging, and tracking of ophthalmological diseases. Whereas automatic segmentation methods are typically much faster than manual segmentation, they may still take several minutes to segment a three-dimensional macular scan, and this can be prohibitive for routine clinical application. In this paper, we propose a fast, multi-layer macular OCT segmentation method based on a fast level set method. In our framework, the boundary evolution operations are computationally fast, are specific to each boundary between retinal layers, guarantee proper layer ordering, and avoid level set computation during evolution. Subvoxel resolution is achieved by reconstructing the level set functions after convergence. Experiments demonstrate that our method reduces the computation expense by 90% compared to graph-based methods and produces comparable accuracy to both graph-based and level set retinal OCT segmentation methods.
Hybrid Cone-Beam Tomographic Reconstruction: Incorporation of Prior Anatomical Models to Compensate for Missing Data

We propose a method for improving the quality of cone-beam tomographic reconstruction done with a C-arm. C-arm scans frequently suffer from incomplete information due to image truncation, limited scan length, or other limitations. Our proposed “hybrid reconstruction” method injects information from a prior anatomical model, derived from a subject-specific computed tomography (CT) or from a statistical database (atlas), where the C-arm X-ray data is missing. This significantly reduces reconstruction artifacts with little loss of true information from the X-ray projections. The methods consist of constructing anatomical models, fast rendering of digitally reconstructed radiograph (DRR) projections of the models, rigid or deformable registration of the model and the X-ray images, and fusion of the DRR and X-ray projections, all prior to a conventional filtered back-projection algorithm. Our experiments, conducted with a mobile image intensifier C-arm, demonstrate visually and quantitatively the contribution of data fusion to image quality, which we assess through comparison to a “ground truth” CT. Importantly, we show that a significantly improved reconstruction can be obtained from a C-arm scan as short as 90° by complementing the observed projections with DRRs of two prior models, namely an atlas and a preoperative same-patient CT. The hybrid reconstruction principles are applicable to other types of C-arms as well.
A convolution backprojection formula for three-dimensional vector tomography

Three-dimensional (3-D) tomographic measurements of vector flow fields are possible using acoustical, optical, and magnetic resonance imaging. A general mathematical framework for this type of data acquisition called the probe transform has been studied by the author in previous work. It has been shown that the irrotational field component can be reconstructed using only one probe direction, and that the solenoidal field component can be reconstructed using only two probe directions. Previously, the reconstruction formulas were of the backprojection-convolution type; that is, first backproject then convolve in 3-D. In this paper a convolution-backprojection formula, which has significant computational savings, is developed. Numerical simulations are also given to demonstrate the performance of this new approach.<<ETX>>
Reconstructing Convex Sets from Support Line Measurements

Algorithms are proposed for reconstructing convex sets given noisy support line measurements. It is observed that a set of measured support lines may not be consistent with any set in the plane. A theory of consistent support lines which serves as a basis for reconstruction algorithms that take the form of constrained optimization algorithms is developed. The formal statement of the problem and constraints reveals a rich geometry that makes it possible to include prior information about object position and boundary smoothness. The algorithms, which use explicit noise models and prior knowledge, are based on maximum-likelihood and maximum a posteriori estimation principles and are implemented using efficient linear and quadratic programming codes. Experimental results are presented. This research sets the stage for a more general approach to the incorporation of prior information concerning the estimation of object shape.
Topology preserving brain tissue segmentation using graph cuts

In segmentation of magnetic resonance brain images, it is important to maintain topology of the segmented structures. In this work, we present a framework to segment multiple objects in a brain image while preserving the topology of each object as given in an initial topological template. The framework combines the advantages of digital topology and several existing techniques in graph cuts segmentation. The proposed technique can handle any given topology and enforces object-level relationships with little constraint over the geometry. We apply our algorithm to brain tissue segmentation and demonstrate its accuracy and computational efficiency.
Phase Vector Incompressible Registration Algorithm for Motion Estimation From Tagged Magnetic Resonance Images

Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. It is challenging to obtain 3-D motion estimates due to a tradeoff between image slice density and acquisition time. Typically, interpolation methods are used either to combine 2-D motion extracted from sparse slice acquisitions into 3-D motion or to construct a dense volume from sparse acquisitions before image registration methods are applied. This paper proposes a new phase-based 3-D motion estimation technique that first computes harmonic phase volumes from interpolated tagged slices and then matches them using an image registration framework. The approach uses several concepts from diffeomorphic image registration with a key novelty that defines a symmetric similarity metric on harmonic phase volumes from multiple orientations. The material property of harmonic phase solves the aperture problem of optical flow and intensity-based methods and is robust to tag fading. A harmonic magnitude volume is used in enforcing incompressibility in the tissue regions. The estimated motion fields are dense, incompressible, diffeomorphic, and inverse-consistent at a 3-D voxel level. The method was evaluated using simulated phantoms, human brain data in mild head accelerations, human tongue data during speech, and an open cardiac data set. The method shows comparable accuracy to three existing methods while demonstrating low computation time and robustness to tag fading and noise.
Tracking tongue motion in three dimensions using tagged MR image

Harmonic phase (HARP) analysis has been used in tagged magnetic resonance imaging (MRI) to measure two-dimensional (2D) in-plane motion and strain, and was recently applied in characterizing the motion of tongue during speech. The 3D-HARP method extended the HARP method to track three-dimensional (3D) cardiac motion from short- and long-axis tagged MR images by diffusing 2D in-plane motion on a sparse 3D mesh. In this paper, we propose a new 3D-HARP method to calculate the 3D tongue motion from three orthogonal tag orientations imaged within sagittal, coronal, and axial image planes. Our method iteratively tracks 2D in-plane motions on points on a compact mesh using 2D-HARP followed by thin-plate spline (TPS) interpolation to extend the 2D motion to the whole mesh. Experiments on real tongue data show that our method is capable of accurate motion tracking in simple utterances
Experiments in multiresolution motion estimation for multifrequency tagged cardiac MR images

In tagged magnetic resonance imaging of the human heart, different frequency patterns can be imposed and imaged as the heart moves. It is advantageous to use low-frequency patterns to improve both image acquisition time and the performance of gradient-based optical flow motion analysis methods. This paper explores the effects of three factors-image prefiltering, multiresolution optical flow, and multifrequency tag patterns through a serious of controlled simulation experiments. We show that use of image prefiltering in a multiresolution optical flow approach achieves significant performance gains, and permits a factor of four reduction in imaging time, without the need for multifrequency tag patterns.