nils-herrmann/clean_scirepeval huggingface dataset:
Joint cardiac and respiratory motion correction and super-resolution reconstruction in coronary PET/CT

Coronary artery disease is marked by the development of chronic inflammation in the vascular arteries that is associated with coronary plaques. Positron emission tomography (PET) is capable of detecting inflammation through activated macrophage uptake of FDG. Unfortunately, in conventional cardiac PET, respiratory and cardiac motion during acquisition leads to severe blurring of the resulting images and an effective spatial resolution inadequate for plaque detection and localization. In this paper, we extend our previous image-domain approach to a fully integrated, data-domain method that starts from the observed projection data and performs a model-based inversion and motion correction of all the data to create a high-resolution focused cardiac image. We term the new approach Data-domain Cardiac Shape Tracking and Adjustment for Respiration or D-CSTAR. In contrast to existing image domain methods the image reconstruction and motion correction steps are not separated. Unlike current data domain methods both cardiac and respiratory motions are compensated for. In D-CSTAR, cardiac motion parameters are estimated from X-ray CT images acquired in a breath-hold state. This cardiac motion information is incorporated in a unified PET reconstruction functional which jointly estimates and corrects for respiratory motion, compensates for phase aligned cardiac motion, and super-resolves the image. The technique is presented and applied to simulated cardiac PET/CT data corresponding to the XCAT phantom with both cardiac and respiratory cycles. The results show a marked qualitative and quantitative improvement when compared to conventional and existing PET methods.
Multiscale hypothesis testing with application to anomaly characterization from tomographic projections

Anomaly characterization from tomographic measurements is of interest in a wide range of fields. In this paper we address the problems of single anomaly detection and localization which we formulate as hypothesis testing problems. While the optimal hypothesis test is easy to formulate, it is computationally infeasible due to the overwhelming number of hypotheses which must be considered. We propose the multiscale hypothesis test (MSHT) as an efficient suboptimal alternative. We show how to find decision statistics to achieve maximal composite hypothesis distinguishability for the composite hypothesis tests which comprise the MSHT.
Reconstructing polygons from moments with connections to array processing

We establish a set of results showing that the vertices of any simply connected planar polygonal region can be reconstructed from a finite number of its complex moments. These results find applications in a variety of apparently disparate areas such as computerized tomography and inverse potential theory, where in the former, it is of interest to estimate the shape of an object from a finite number of its projections, whereas in the latter, the objective is to extract the shape of a gravitating body from measurements of its exterior logarithmic potentials at a finite number of points. We show that the problem of polygonal vertex reconstruction from moments can in fact be posed as an array processing problem, and taking advantage of this relationship, we derive and illustrate several new algorithms for the reconstruction of the vertices of simply connected polygons from moments.
Multiscale segmentation and anomaly enhancement of SAR imagery

We present efficient multiscale approaches to the segmentation of natural clutter, specifically grass and forest, and to the enhancement of anomalies in synthetic aperture radar (SAR) imagery. The methods we propose exploit the coherent nature of SAR sensors. In particular, they take advantage of the characteristic statistical differences in imagery of different terrain types, as a function of scale, due to radar speckle. We employ a class of multiscale stochastic processes that provide a powerful framework for describing random processes and fields that evolve in scale. We build models representative of each category of terrain of interest (i.e., grass and forest) and employ them in directing decisions on pixel classification, segmentation, and anomalous behaviour. The scale-autoregressive nature of our models allows extremely efficient calculation of likelihoods for different terrain classifications over windows of SAR imagery. We subsequently use these likelihoods as the basis for both image pixel classification and grass-forest boundary estimation. In addition, anomaly enhancement is possible with minimal additional computation. Specifically, the residuals produced by our models in predicting SAR imagery from coarser scale images are theoretically uncorrelated. As a result, potentially anomalous pixels and regions are enhanced and pinpointed by noting regions whose residuals display a high level of correlation throughout scale. We evaluate the performance of our techniques through testing on 0.3-m resolution SAR data gathered with Lincoln Laboratory's millimeter-wave SAR.
Image segmentation based on prior probabilistic shape models

In the domain of shape modeling researchers have investigated various shape models in the abstract. In particular, Zhu and co-workers have proposed a maximum-entropy based model of object shape derived from data. In this paper we investigate the possibility of using such a shape model for image segmentation. To our knowledge, this is the first attempt at using such models to solve practical problems. One of the challenges of these models is their computational cost. We develop an efficient Monte Carlo Markov Chain (MCMC) method to train the model. A Bayesian framework is then used to perform segmentation based on the identified prior shape model and observed data. Based on limited evaluation we can demonstrate superior segmentation results relative to segmentations performed with no prior information and segmentations performed using a curve evolution approach with a generic boundary length penalizing prior.
A statistical framework for space‐based EUV ionospheric tomography

We present a statistical reconstruction framework for space‐based extreme ultraviolet (EUV) ionospheric tomography. The EUV technique offers a means to invert the nighttime F region electron density on global scales from a single spaceborne spectrograph, using prominent optically thin emissions produced by radiative recombination of O+. Since the EUV technique does not rely on ground receivers to make the measurements, the observations do not suffer from limitations on the viewing angles. The EUV tomography is an ill‐conditioned inverse problem in the sense that its solution is sensitive to perturbations of the measured data. With large condition numbers of a typical projection matrix, simple least squares inversion techniques yield unacceptable results in the presence of noise. This reflects the fact that more degrees of freedom are being sought than are supported by the noisy data. To overcome this limitation, we cast the tomographic inverse problem in a stochastic framework and incorporate a statistical prior model. In doing so we also obtain measures of estimation uncertainty for the solutions. Through simulations, we demonstrate the applicability of these techniques in the context of a space mission designed for EUV ionospheric tomography, namely, the Tomographic Experiment Using Radiative Recombinative Ionospheric EUV and Radio Sources (TERRIERS). The simulations show promising results for EUV tomography as a viable ionospheric remote sensing technique.
A recursive estimation approach to the segmentation of MR imagery

Magnetic resonance imaging (MRI) has become a widely used research and clinical tool in the study of the human brain. The ability to accurately segment the MRI data set into homogeneous regions such as gray matter, white matter, and cerebro spinal fluid aids in morphological quantification of brain features. The large amount of data associated with typical MRI brain scans makes completely manual segmentation prohibitive on a large scale. We develop an estimation-theoretic interpretation of the segmentation problem which leads to a computationally efficient, statistically based recursive technique for its solution. Being statistically based, the method also provides associated measures of uncertainty of the resulting estimates, which are useful both for evaluation of the estimates as well as their combination with other sources of information.
A variational approach for reconstructing low dose images in clinical helical CT

Exposure to medical radiation is an important healthcare concern. In this paper we present a variational formulation for image reconstruction from low-dose transmission X-ray CT as well as its application to real clinical data. Our projection domain reconstruction method is based on high-order total variation penalties. We apply the method to clinical neck data obtained from a current 64-channel MDCT helical scanner, where small features and large dynamic range variation are pervasive. The ability to generate high-quality images even at 75% dose reduction is demonstrated. In particular, our approach exhibits image SNR comparable to full-dose filtered backprojection images while avoiding the obvious stair case and blockiness artifacts typically encountered in such methods.
Discrete image reconstruction for material quantification

Imaging and quantifying elements of the internal structure of a sample is important for the analysis and engineering of materials. Electron tomography is a powerful technique that can reveal the three-dimensional structure of a sample at the nanometer scale. Unfortunately, the limited quantity and quality of electronic tomographic data produces artifacts in conventional reconstructions that can confound subsequent segmentation and quantitation. An alternative approach, termed discrete tomography, is to directly reconstruct only a limited and discrete set of pixel amplitudes. Recently, graph cut methods have been used with great success on such discrete-label problems in the domain of image segmentation. This work aims to develop such a graph-cut framework for discrete tomography. We analyze the structure of linear inverse problems, showing the relationship of the sensing structure to graph non-representability. We then propose a method that iteratively minimizes a surrogate energy functional that is always graph-representable. We show reconstruction results using synthetic phantom images for limited angle scenarios and compare them to a conventional reconstruction technique.
Polygon reconstruction from moments using array processing

We prove a set of results showing that the vertices of any simply-connected planar polygonal region can be reconstructed from a finite number of its complex moments using array processing. In particular, we derive and illustrate several new algorithms for the reconstruction of the vertices of simply-connected polygons from moments. These results find applications in a variety of apparently disparate areas such as computerized tomography and inverse potential theory, where in the former it is of interest in estimating the shape of an object from a finite number of its projections; while in the latter, the objective is to extract the shape of a gravitating body from measurements of its exterior logarithmic potentials at a finite number of points. The applications of the algorithms, we develop, to tomography hence expose a seemingly deep connection between the fields of tomography and array processing. This connection implies that a host of numerical algorithms such as MUSIC, Min-norm, and Prony are now available for application to tomographic reconstruction problems.<<ETX>>
Multiresolution models for random fields and their use in statistical image processing

We describe a probabilistic framework for optimal multiresolution processing and analysis of spatial phenomena. Our developed multiresolution (MR) models are useful in describing random processes and fields. The scale recursive nature of the resulting models, leads to extremely efficient algorithms for optimal estimation and likelihood calculation. These models, which are described, have also provided a framework for data fusion, and produced new solutions to problems in computer vision (optical flow estimation), remote sensing (oceanography where dimensional complexity is in thousands), and various inverse problems of mathematical physics.
Edge-preserving image reconstruction for coherent imaging applications

We propose a method for edge-preserving regularized reconstruction in coherent imaging systems. In our framework, image formation from measured data is achieved through the minimization of a cost function, which includes nonquadratic regularizing constraints for suppressing noise artifacts, while preserving the object boundaries in the reconstruction. The cost function we use effectively deals with the complex-valued and random-phase nature of the scattered field, which is inherent in many coherent systems. We solve the challenging optimization problems posed in our framework by a novel extension of half-quadratic regularization methods. We present experimental results from three coherent imaging applications: digital holography, synthetic aperture radar, and medical ultrasound. The proposed technique produces images where coherent speckle artifacts are effectively suppressed, and boundaries between different regions in the scene are preserved.
A Spatio-Temporal Deconvolution Method to Improve Perfusion CT Quantification

Perfusion imaging is a useful adjunct to anatomic imaging in numerous diagnostic and therapy-monitoring settings. One approach to perfusion imaging is to assume a convolution relationship between a local arterial input function and the tissue enhancement profile of the region of interest via a ¿residue function¿ and subsequently solve for this residue function. This ill-posed problem is generally solved using singular-value decomposition based approaches, and the hemodynamic parameters are solved for each voxel independently. In this paper, we present a formulation which incorporates both spatial and temporal correlations, and show through simulations that this new formulation yields higher accuracy and greater robustness with respect to image noise. We also show using rectal cancer tumor images that this new formulation results in better segregation of normal and cancerous voxels.
Moderately rough dielectric interface profile reconstruction via short-pulse quasi-ray Gaussian beams

A new technique for estimating the coarse-scale profile of a moderately rough interface between air and a homogeneous dielectric halfspace is presented. The proposed approach is based on space-time sparsely sampled reflected field observations and uses a quasi-ray Gaussian beam fast-forward model, coupled with a compact parameterization of the surface profile in terms of B-splines, from which the profile estimation problem is posed as a nonlinear optimization problem. Numerical experiments are presented to assess accuracy, reliability, and computational efficiency. The proposed approach finds applications in adaptive schemes for rough surface underground imaging of shallowly buried targets via ultra wide-band ground penetrating radars.
Multiresolution stochastic processing of Topex/Poseidon oceanographic altimetry

Remarkable advances in remote sensing are producing measurements on increasingly finer grids, offering the possibility of a far better understanding and characterization of the ocean surface. The sheer quantity of measurements has rendered traditional data smoothing/assimilation algorithms obsolete however, even on high performance workstations; the basis of this paper is the application of an extremely fast multiresolution smoothing framework to ocean altimetry data. Using the previously developed multiresolution framework, a simple multiscale model is developed which captures the characteristic power-law spectrum of the ocean surface. Smoothing altimetric data and computing error variances on a grid of 512 by 512 points is accomplished in about 10 seconds on a SUN Sparc-10. Finally, further applications of this framework within the oceanographic context are discussed, including model identification and anomaly detection.<<ETX>>
A statistical method for discrimination of natural terrain types from SAR data

We propose a method for discrimination of underlying textural structures from spotlight-mode synthetic aperture radar (SAR) returns by using a tomographic data acquisition model as the basis for statistical reasoning. We model the hypothesized textures by statistically self-similar processes and formulate the problem in a hypothesis testing framework in the SAR range profile domain without any image formation. We achieve a near-optimal, computationally efficient evaluation of the likelihood test by transforming the data into the multiscale domain.
MRF-based algorithms for segmentation of SAR images

In this paper we demonstrate a new method for Bayesian image segmentation, with specific application to synthetic aperture radar (SAR) imagery, and we compare its performance to conventional Bayesian segmentation methods. Segmentation can be an important feature extraction technique in recognition problems, especially when we can incorporate prior information to improve the segmentation. Markov random field (MRF) approaches are widely studied for segmentation, but they can be computationally expensive and, hence, are not widely used in practice. This computational burden is similar to that seen in the statistical mechanics simulation of simple MRF models such as certain magnetic models. Recently, Swendsen and Wang (1997) and others have had great success accelerating these simulations using so-called "cluster" Monte Carlo methods. We show that these cluster algorithms can provide speed improvements over conventional MRF methods when the MRF prior model has sufficient weight relative to the observation model.
A theory for multiscale stochastic realization

Describes a generalisation theory for a class of multiscale stochastic processes. This realisation problem represents a non-trivial variation on the standard time series stochastic realisation problem. The authors generalize some Gaussian Markov random fields representation results, thereby developing a fuller understanding of what types of stochastic phenomena can be usefully captured by the multiscale framework and how the corresponding models can be built. The authors' theory for multiscale stochastic realization is based on a novel application of the techniques of canonical correlation analysis. These techniques, which rely heavily on the singular value decomposition, constitute a standard analytical tool in both multivariate statistics and in the field of reduced-order modeling of linear systems. Using canonical correlation theory, the authors demonstrate that any given l-D or 2-D Gaussian random process, Markov or otherwise, can be represented by some multiscale model. More importantly, the authors also introduce a family of approximate representations.<<ETX>>
Dynamic object-based tomographic reconstruction

In this paper, we propose an object-based variational method for dynamic tomography. We model the dynamics of object boundaries with an affined transform and the dynamics of intensities with an autoregressive model. Based on these dynamic models, an energy function is defined which incorporates both the observed data, the prior knowledge about shape and intensity dynamics and prior knowledge about shape smoothness. A coordinate descent algorithm based on curve evolution methods is then developed to jointly estimate the underlying object boundary and intensity sequence. Efficient level set methods are used to implement the curve evolution process. In practice, we may not know the model of shape dynamics. We use our models to develop a gradient descent algorithm for the estimation of the dynamic model of shape from a training sequence of dynamic shapes. Preliminary experimental results demonstrate the strength of the algorithm in reconstructing dynamic objects from very sparse and noisy data.
Object-based reconstruction using coupled tomographic flows

We develop a new geometric curve evolution approach to tomographic reconstruction problems including multiple objects having different levels of intensity, and background with varying intensity. We use two contours to represent the boundaries of distinct object regions with different intensity levels and a set of basis functions and expansion coefficients to describe the background. By designing coupled "tomographic flows", the problem of estimating the two contours and intensity coefficients is recast into a curve evolution problem. A new "repelling" force between the two coupled curves is proposed for the reconstruction algorithms to accelerate convergence. The performance of the coupled curve evolution method is demonstrated via a Radon inversion example. The results show that our curve evolution method achieves improved shape reconstruction over comparable regularization techniques.
Unified Anomaly Suppression and Boundary Extraction in Laser Radar Range Imagery based on a Joint Curve-Evolution and Expectation-Maximization Algorithm

In this paper, we develop a new unified approach for laser radar range anomaly suppression, range profiling, and segmentation. This approach combines an object-based hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as a minimization problem which jointly estimates the target boundary together with the target region range variation and background range variation directly from the noisy and anomaly-filled range data. This formulation allows direct incorporation of prior information concerning the target boundary, target ranges, and background ranges into an optimal reconstruction process. Curve evolution techniques and a generalized expectation-maximization algorithm are jointly employed as an efficient solver for minimizing the objective energy, resulting in a coupled pair of object and intensity optimization tasks. The method directly and optimally extracts the target boundary, avoiding a suboptimal two-step process involving image smoothing followed by boundary extraction. Experiments are presented demonstrating that the proposed approach is robust to anomalous pixels (missing data) and capable of producing accurate estimation of the target boundary and range values from noisy data.
A wavelet-based method for multiscale tomographic reconstruction

The authors represent the standard ramp filter operator of the filtered-back-projection (FBP) reconstruction in different bases composed of Haar and Daubechies compactly supported wavelets. The resulting multiscale representation of the ramp-filter matrix operator is approximately diagonal. The accuracy of this diagonal approximation becomes better as wavelets with larger numbers of vanishing moments are used. This wavelet-based representation enables the authors to formulate a multiscale tomographic reconstruction technique in which the object is reconstructed at multiple scales or resolutions. A complete reconstruction is obtained by combining the reconstructions at different scales. The authors' multiscale reconstruction technique has the same computational complexity as the FBP reconstruction method. It differs from other multiscale reconstruction techniques in that (1) the object is defined through a one-dimensional multiscale transformation of the projection domain, and (2) the authors explicitly account for noise in the projection data by calculating maximum a posteriori probability (MAP) multiscale reconstruction estimates based on a chosen fractal prior on the multiscale object coefficients. The computational complexity of this maximum a posteriori probability (MAP) solution is also the same as that of the FBP reconstruction. This result is in contrast to commonly used methods of statistical regularization, which result in computationally intensive optimization algorithms.