nils-herrmann/clean_scirepeval huggingface dataset:
Accelerated high b-value diffusion-weighted MR imaging via phase-constrained low-rank tensor model

High b-value Diffusion-weighted MRI (DWI) is promising in cancer imaging but suffers from long acquisition time and low signal-to-noise ratio (SNR). We propose a low-rank tensor model that exploits correlation across both diffusion-induced signal decays and neighboring k-space samples, to accelerate the acquisition of DWI using an extended range of b-values (0 s/mm2 to 2500 s/mm2) and limited (orthogonal only) diffusion directions, an imaging scheme that is increasingly used for brain gliomas evaluation. A phase constraint accounts for phase variations between b-values is also applied. Our method integrates parallel imaging and partial Fourier acquisition naturally, and undersamples along phase-encoding direction only. Reconstruction results using both patient and simulated data with an acceleration factor of 8 show improved SNR and reduced aliasing, as compared to parallel imaging only method as well as two other low-rank model-based methods.
Segmented attenuation correction for PET

The authors describe a hybrid measured/calculated method for attenuation correction in positron emission tomography (PET). This unified reconstruction/segmentation method is based on a penalized weighted least-squares objective function that is minimized using iterative coordinate-descent. Two penalty functions are compared: one for a discrete object parameterization, the other for a continuous parameterization. Simulations demonstrate that the methods can reduce the additional emission image variance typically introduced by noisy attenuation correction factors.<<ETX>>
Maximum-likelihood transmission image reconstruction for overlapping transmission beams

In many transmission imaging geometries, the transmitted "beams" of photons overlap on the detector, such that a detector element may record photons that originated in different sources or source locations and thus traversed different paths through the object. Examples include systems based on scanning line sources or on multiple parallel rod sources. The overlap of these beams has been disregarded by both conventional analytical reconstruction methods as well as by previous statistical reconstruction methods. The authors propose a new algorithm for statistical image reconstruction of attenuation maps that explicitly accounts for overlapping beams in transmission scans. The algorithm is guaranteed to monotonically increase the objective function at each iteration. The availability of this algorithm enables the possibility of deliberately increasing the beam overlap so as to increase count rates. Simulated single photon emission tomography transmission scans based on a multiple line source array demonstrate that the proposed method yields improved resolution/noise tradeoffs relative to "conventional" reconstruction algorithms, both statistical and nonstatistical.
Asymptotic Source Detection Performance of Gamma-Ray Imaging Systems Under Model Mismatch

Likelihood-based test statistics for the task of detecting a radioactive source in background using a gamma-ray imaging system often have intractable distributions. This complicates the tasks of predicting detection performance and setting thresholds that ensure desired false-alarm rates. Asymptotic distributions of test statistics can aid in predicting performance and in setting detection thresholds. However, in applications with complex sensors, like gamma-ray imaging, often only approximate statistical models for the measurements are available. Standard asymptotic approximations can yield inaccurate performance predictions when based on misspecified models. This paper considers asymptotic properties of detection tests based on maximum likelihood (ML) estimates under model mismatch, i.e., when the statistical model used for detection differs from the true distribution. We provide general expressions for the asymptotic distribution of likelihood-based test statistics when the number of measurements is Poisson, and expressions specific to gamma-ray source detection that one can evaluate using a modest amount of data from a real system or Monte Carlo simulation. Considering a simulated Compton imaging system, we show that the proposed expressions yield more accurate detection performance predictions than previous expressions that ignore model mismatch. These expressions require less data and computation than conventional empirical methods.
Detection of Event-Related Spectral Changes in Electrocorticograms

The University of Michigan Direct Brain Interface (UM-DBI) project seeks to detect voluntarily produced electrocortical activity (ECoG) related to actual or imagined movements in humans as the basis for a DBI. In past work we have used cross-correlation based template matching (CCTM) as the method for detecting event-related potentials (ERPs). That approach ignores event-related spectral changes in the ECoG signal. This paper discusses model-based signal detection methods that exploit event-related spectral changes. In particular we propose a quadratic detector based on a two-class hypothesis test with different covariances for the two classes. The covariance matrices are generated by fitting autoregressive (AR) models to training data. Preliminary results show that the quadratic detector yields more channels with good detection performance than the CCTM method, particularly when we impose constraints on detection delay
Estimating 3-D Respiratory Motion From Orbiting Views by Tomographic Image Registration

Respiratory motion remains a significant source of errors in treatment planning for the thorax and upper abdomen. Recently, we proposed a method to estimate two-dimensional (2-D) object motion from a sequence of slowly rotating X-ray projection views, which we called deformation from orbiting views (DOVs). In this method, we model the motion as a time varying deformation of a static prior of the anatomy. We then optimize the parameters of the motion model by maximizing the similarity between the modeled and actual projection views. This paper extends the method to full three-dimensional (3-D) motion and cone-beam projection views. We address several practical issues for using a cone-beam computed tomography (CBCT) scanner that is integrated in a radiotherapy system, such as the effects of Compton scatter and the limited gantry rotation for one breathing cycle. We also present simulation and phantom results to illustrate the performance of this method
Maximum likelihood transmission image reconstruction for overlapping transmission beams

In many transmission imaging geometries, the transmitted "beams" of photons overlap on the detector, such that a detector element may record photons that originated in different sources or source locations and thus traversed different paths through the object, Examples include systems based on scanning line sources or on multiple parallel rod sources. The overlap of these beams has been disregarded by both conventional analytical reconstruction methods as well as by previous statistical reconstruction methods. We propose a new algorithm for statistical image reconstruction of attenuation maps that explicitly accounts for overlapping beams in transmission scans. The algorithm is guaranteed to monotonically increase the objective function at each iteration. The availability of this algorithm enables the possibility of deliberately increasing the beam overlap so as to increase count rates. Simulated SPECT transmission scans based on a multiple line source array demonstrate that the proposed method yields improved resolution/noise tradeoffs relative to "conventional" reconstruction algorithms, both statistical and nonstatistical.
Regularized parallel mri reconstruction using an alternating direction method of multipliers

Using sparsity-based regularization to improve magnetic resonance image (MRI) reconstruction quality demands computation-intensive nonlinear optimization. In this paper, we develop an iterative algorithm based on the method of multipliers–augmented Lagrangian (AL) formalism–for reconstruction from sensitivity encoded data using sparsity-based regularization. We first convert the unconstrained reconstruction problem into an equivalent constrained optimization task and attack the constrained version in an AL framework using an alternating direction minimization method–this leads to an alternating direction method of multipliers whose intermediate steps are amenable to parallelization. Numerical experiments with in-vivo human brain data illustrate that the proposed algorithm converges faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA.
Segmentation-free statistical image reconstruction for polyenergetic X-ray computed tomography

This paper describes a statistical iterative reconstruction method for X-ray CT based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. The algorithm accommodates mixtures of tissues with known mass attenuation coefficients but unknown densities. We formulate a penalized-likelihood approach for this polyenergetic model based on Poisson statistics.
Statistical Sinogram Restoration in Dual-Energy CT for PET Attenuation Correction

Dual-energy (DE) X-ray computed tomography (CT) has been found useful in various applications. In medical imaging, one promising application is using low-dose DECT for attenuation correction in positron emission tomography (PET). Existing approaches to sinogram material decomposition ignore noise characteristics and are based on logarithmic transforms, producing noisy component sinogram estimates for low-dose DECT. In this paper, we propose two novel sinogram restoration methods based on statistical models: penalized weighted least square (PWLS) and penalized likelihood (PL), yielding less noisy component sinogram estimates for low-dose DECT than classical methods. The proposed methods consequently provide more precise attenuation correction of the PET emission images than do previous methods for sinogram material decomposition with DECT. We report simulations that compare the proposed techniques and existing approaches.
Adaptive sparse modeling and shifted-poisson likelihood based approach for low-dosect image reconstruction

Recent research in computed tomographic imaging has focused on developing techniques that enable reduction of the X-ray radiation dose without loss of quality of the reconstructed images or volumes. While penalized weighted-least squares (PWLS) approaches have been popular for CT image reconstruction, their performance degrades for very low dose levels due to the inaccuracy of the underlying WLS statistical model. We propose a new formulation for low-dose CT image reconstruction based on a shifted-Poisson model based likelihood function and a data-adaptive regularizer using the sparsifying transform model for images. The sparsifying transform is pre-learned from a dataset of patches extracted from CT images. The nonconvex cost function of the proposed penalized-likelihood reconstruction with sparsifying transforms regularizer (PL-ST) is optimized by alternating between a sparse coding step and an image update step. The image update step deploys a series of convex quadratic majorizers that are optimized using a relaxed linearized augmented Lagrangian method with ordered-subsets, reducing the number of (expensive) forward and backward projection operations. Numerical experiments show that for low dose levels, the proposed data-driven PL-ST approach outperforms prior methods employing a nonadaptive edge-preserving regularizer. PL-ST also outperforms prior PWLS-ST approach at very low X-ray doses.
Image Reconstruction for Limited-Angle Electron Beam X-Ray Computed Tomography With Energy-Integrating Detectors for Multiphase Flows

We propose a new iterative X-ray computed tomography (CT) reconstruction algorithm for electron beam X-ray tomography of multiphase flows in metal pipes. This application uses limited-angle projections due to the fixed configuration, and semiconductor-type energy-integrating detectors. For the data-fitting objective function, the proposed method incorporates a nonlinear Gaussian model with object-dependent variance to approximate the compound Poisson distribution, and a dual material decomposition based on images of the volume fractions of metal (titanium) and liquid (water). The volume fraction-based material decomposition enables us to use a maximum sum constraint that helps address the ill-posed nature of the problem. Two different regularizers,  $\ell _0$ norm and edge-preserving hyperbola regularizers, are applied differently on each volume fraction image based on the characteristics of objects in each image. A synthetic phantom simulation illustrates that the proposed algorithm enables the aforementioned CT system to achieve high quality images by minimizing artifacts induced by limited-angle data and beam hardening.
Statistical image reconstruction methods for simultaneous emission/transmission PET scans

Transmission scans are necessary for estimating the attenuation correction factors (ACFs) to yield quantitatively accurate PET emission images. To reduce the total scan time, post-injection transmission scans have been proposed in which one can simultaneously acquire emission and transmission data using rod sources and sinogram windowing. However, since the post-injection transmission scans are corrupted by emission coincidences, accurate correction for attenuation becomes more challenging. Conventional methods (emission subtraction) for ACF computation from post-injection scans are suboptimal and require relatively long scan times. The authors introduce statistical methods based on penalized-likelihood objectives to compute ACFs and then use them to reconstruct lower noise PET emission images from simultaneous transmission/emission scans. Simulations show the efficacy of the proposed methods. These methods improve image quality and SNR of the estimates as compared to conventional methods.
Simplified digital holographic reconstruction using statistical methods

For reconstructing a complex object wavefront from digital holograms, we propose a new penalized-likelihood approach based on the measurement statistics and edge-preserving regularization. The log-likelihood is complicated since the measurements are related to the magnitude of the complex beam. We use optimization transfer to derive a new simplified iterative algorithm that monotonically decreases the cost function. Unlike the conventional FFT-based holographic reconstruction method, the new approach uses all of the measured data and can be applied to holograms with any (known) reference beam pattern. Simulation results demonstrate the potential for improved image quality.
A new statistical image reconstruction algorithm for polyenergetic X-ray CT

This paper presents a new statistical reconstruction algorithm for X-ray CT. The algorithm is based on Poisson statistics and a physical model that accounts for the measurement nonlinearities caused by energy-dependent attenuation. We model each voxel's attenuation as a mixture of bone and soft tissue by defining density-dependent tissue fractions, maintaining one unknown per voxel avoiding the need of a pre-segmentation. Rather than requiring the entire X-ray spectrum, the method approximates the 2D beam hardening function corresponding to bone and soft tissue with the 1D function corresponding to water and one or two empirical tuning parameters. Results on simulated human data (NCAT phantom) showed a beam hardening reduction similar to conventional post-processing techniques, but with an improved signal to noise ratio.
Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities

In magnetic resonance imaging, magnetic field inhomogeneities cause distortions in images that are reconstructed by conventional fast Fourier transform (FFT) methods. Several noniterative image reconstruction methods are used currently to compensate for field inhomogeneities, but these methods assume that the field map that characterizes the off-resonance frequencies is spatially smooth. Recently, iterative methods have been proposed that can circumvent this assumption and provide improved compensation for off-resonance effects. However, straightforward implementations of such iterative methods suffer from inconveniently long computation times. This paper describes a tool for accelerating iterative reconstruction of field-corrected MR images: a novel time-segmented approximation to the MR signal equation. We use a min-max formulation to derive the temporal interpolator. Speedups of around 60 were achieved by combining this temporal interpolator with a nonuniform fast Fourier transform with normalized root mean squared approximation errors of 0.07%. The proposed method provides fast, accurate, field-corrected image reconstruction even when the field map is not smooth.
Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms

Most expectation-maximization (EM) type algorithms for penalized maximum-likelihood image reconstruction converge slowly, particularly when one incorporates additive background effects such as scatter, random coincidences, dark current, or cosmic radiation. In addition, regularizing smoothness penalties (or priors) introduce parameter coupling, rendering intractable the M-steps of most EM-type algorithms. This paper presents space-alternating generalized EM (SAGE) algorithms for image reconstruction, which update the parameters sequentially using a sequence of small "hidden" data spaces, rather than simultaneously using one large complete-data space. The sequential update decouples the M-step, so the maximization can typically be performed analytically. We introduce new hidden-data spaces that are less informative than the conventional complete-data space for Poisson data and that yield significant improvements in convergence rate. This acceleration is due to statistical considerations, not numerical overrelaxation methods, so monotonic increases in the objective function are guaranteed. We provide a general global convergence proof for SAGE methods with nonnegativity constraints.
Simplified statistical image reconstruction for polyenergetic X-ray CT

In X-ray computed tomography (CT), bony structures cause beam-hardening artifacts that appear on the reconstructed image as streaks and shadows. Currently, there are two classes of methods for correcting for bone-related beam hardening. The standard approach used with filtered backprojection (FBP) reconstruction is the Joseph and Spital (JS) method. In the current simulation study (which is inspired by a clinical head scan), the JS method requires a simple table or polynomial model for correcting water-related beam hardening, and two additional tuning parameters to compensate for bone. Like all FBP methods, it is sensitive to data noise. Statistical methods have also been proposed recently for image reconstruction from noisy polyenergetic X-ray data. However, these methods have required more knowledge of the X-ray spectrum than is needed in the JS method, hampering their use in practice. This paper proposes a simplified statistical image reconstruction approach for polyenergetic X-ray CT that uses the same calibration data and tuning parameters used in the JS method, thereby facilitating its practical use. Simulation results indicate that the proposed method provides improved image quality (reduced beam hardening artifacts and noise) compared to the JS method, at the price of increased computation. The results also indicate that the image quality of the proposed method is comparable to a method requiring more beam-hardening information.
3-D Monte Carlo-based scatter compensation in quantitative I-131 SPECT reconstruction

We have implemented highly accurate Monte Carlo based scatter modeling (MCS) with 3-D ordered subsets expectation maximization (OSEM) reconstruction. The scatter is included in the statistical model as an additive term and attenuation and detector response are included in the forward/backprojector. In the present implementation of MCS, a simple multiple window-based estimate is used for the initial iterations and in the later iterations the Monte Carlo estimate is used for several iterations before it is updated. For I-131, MCS was evaluated and compared with triple energy window (TEW) scatter compensation using simulation studies of a mathematical phantom and a clinically realistic voxel-phantom. Even after just two Monte Carlo runs, excellent agreement was found between the MCS estimate and the true scatter distribution. Accuracy and noise of the reconstructed images were superior with MCS compared to TEW. However, the improvement was not large, and in some cases may not justify the large computational requirements of MCS. Finally clinical application of MCS was demonstrated by applying the method to a radioimmunotherapy (RIT) patient study.
Joint estimation of attenuation and emission images from PET scans

In modern PET scanners, image reconstruction is performed sequentially in two steps regardless of the reconstruction method: 1. Attenuation correction factor computation (ACF) from transmission scans, 2. Emission image reconstruction using the computed ACFs. This reconstruction scheme does not use all the information in the transmission and emission scans. Post-injection transmission scans contain emission contamination which includes information about emission parameters. Conversely emission scans contain information about the attenuating medium. To use all the available information, the authors propose a joint estimation approach that estimates the attenuation map and the emission image from these two scans. The penalized-likelihood objective function is nonconvex for this problem. The authors propose an algorithm based on paraboloidal surrogates that alternates between emission and attenuation parameters and is guaranteed to monotonically decrease the objective function.
A fast recursive algorithm for computing CR-type bounds for image reconstruction problems

The authors describe a method for computing matrix CR bounds for image reconstruction problems using an iterative algorithm that avoids the intractable inversion of the Fisher matrix required by direct methods. The algorithm produces a close approximation to the CR bound, requiring only O(n/sup 2/) floating point operations per pixel of interest, an order of magnitude savings relative to the O(n/sup 3/) flops required by noniterative methods. To illustrate the utility of the iterative algorithm, a prototypical application is studied: the dependence of achievable reconstruction accuracy on angular and radial sampling.<<ETX>>
Nonuniform fast Fourier transforms using min-max interpolation

The fast Fourier transform (FFT) is used widely in signal processing for efficient computation of the FT of finite-length signals over a set of uniformly spaced frequency locations. However, in many applications, one requires nonuniform sampling in the frequency domain, i.e., a nonuniform FT. Several papers have described fast approximations for the nonuniform FT based on interpolating an oversampled FFT. This paper presents an interpolation method for the nonuniform FT that is optimal in the min-max sense of minimizing the worst-case approximation error over all signals of unit norm. The proposed method easily generalizes to multidimensional signals. Numerical results show that the min-max approach provides substantially lower approximation errors than conventional interpolation methods. The min-max criterion is also useful for optimizing the parameters of interpolation kernels such as the Kaiser-Bessel function.