nils-herrmann/clean_scirepeval huggingface dataset:
Fast Predictions of Variance Images for Fan-Beam Transmission Tomography With Quadratic Regularization

Accurate predictions of image variances can be useful for reconstruction algorithm analysis and for the design of regularization methods. Computing the predicted variance at every pixel using matrix-based approximations is impractical. Even most recently adopted methods that are based on local discrete Fourier approximations are impractical since they would require a forward and backprojection and two fast Fourier transform (FFT) calculations for every pixel, particularly for shift-variant systems like fan-beam tomography. This paper describes new "analytical" approaches to predicting the approximate variance maps of 2-D images that are reconstructed by penalized-likelihood estimation with quadratic regularization in fan-beam geometries. The simplest of the proposed analytical approaches requires computation equivalent to one backprojection and some summations, so it is computationally practical even for the data sizes in X-ray computed tomography (CT). Simulation results show that it gives accurate predictions of the variance maps. The parallel-beam geometry is a simple special case of the fan-beam analysis. The analysis is also applicable to 2-D positron emission tomography (PET)
A proposed method for correcting aperture penetration in high energy slit aperture and pinhole SPECT

Aperture penetration in high energy pinhole and slit aperture SPECT results in a decrease of both resolution and contrast. At 511 keV, approximately 50% of projection counts can be attributed to penetration. Correction for this effect by measuring the penetration from a "blocked" slit has been investigated using SPRINT-II. Slit apertures were blocked with small bars of depleted uranium (DU) and projections acquired to serve as an estimate of the penetration background. The block's finite size and DU's limited attenuation of 511 keV photons required the blocked projection data to be corrected by a spatially variant, Monte Carlo based window function.  FWTM due to large penetration tails. Lines reconstructed with penetration correction achieved 5.0 mm FWHM and 8.6 mm FWTM resolution.  Images produced with correction produced clear images of 3 mm holes with negligible background The findings should be generally applicable to clinical gamma camera systems.
Deep dictionary-transform learning for image reconstruction

Various dictionaries and transforms are known to sparsify images, and have been exploited in algorithms for image reconstruction from limited data. Methods that simultaneously reconstruct the image and learn dictionaries or sparsifying transforms for image patches, called blind compressed sensing methods, have shown promising performance. Motivated by such adaptive algorithms, this paper proposes an approach to train dictionary-transform based methods for image reconstruction by minimizing a minimum absolute reconstruction error criterion. Each "layer" of the algorithm consists of applying (convolving) trained transforms, thresholdings, and dictionaries to images, followed by a simple least squares update of the images. Numerical experiments illustrate the usefulness and speed-ups provided by such trained algorithms compared to related schemes.
Iterative tomographic image reconstruction using Fourier-based forward and back-projectors

Iterative image reconstruction algorithms play an increasingly important role in modern tomographic systems, especially in emission tomography. With the fast increase of the sizes of the tomographic data, reduction of the computation demands of the reconstruction algorithms is of great importance. Fourier-based forward and back-projection methods have the potential to considerably reduce the computation time in iterative reconstruction. Additional substantial speed-tip of those approaches can be obtained utilizing powerful and cheap off-the-shelf FFT processing hardware. The Fourier reconstruction approaches are based on the relationship between the Fourier transform or the image and Fourier transformation of the parallel-ray projections. The critical two steps are the estimations of the samples of the projection transform, on the central section through the origin of Fourier space, from the samples of the transform of the image, and vice versa for back-projection. Interpolation errors are a limitation of Fourier-based reconstruction methods. We have applied min-max optimized Kaiser-Bessel interpolation within the nonuniform Fast Fourier transform (NUFFT) framework. This approach is particularly well suited to the geometries of PET scanners. Numerical and computer simulation results show that the min-max NUFFT approach provides substantially lower approximation errors in tomographic forward and back-projection than conventional interpolation methods, and that it is a viable candidate for fast iterative image reconstruction.
Optimization transfer approach to joint registration / reconstruction for motion-compensated image reconstruction

Motion artifacts in image reconstruction problems can be reduced by performing image motion estimation and image reconstruction jointly using a penalized-likelihood cost function. However, updating the motion parameters by conventional gradient-based iterations can be computationally demanding due to the system model required in inverse problems. This paper describes an optimization transfer approach that leads to minimization steps for the motion parameters that have comparable complexity to those needed in image registration problems. This approach can simplify the implementation of motion-compensated image reconstruction (MCIR) methods when the motion parameters are estimated jointly with the reconstructed image.
List-mode maximum likelihood reconstruction of Compton scatter camera images in nuclear medicine

A Maximum Likelihood (ML) image reconstruction technique using list-mode data has been applied to Compton scattering camera imaging. List-mode methods are appealing in Compton camera image reconstruction because the total number of data elements in the list (the number of detected photons) is significantly smaller than the number of possible combinations of position and energy measurements, leading to a much smaller problem than that faced by traditional iterative reconstruction techniques. For a realistic size device, the number of possible detector bins can be as large as 10 billion per pixel of the image space, while the number of counted photons would typically be a very small fraction of that. The primary difficulty in applying the list-mode technique is in determining the parameters which describe the response of the imaging system. In this work, a simple method for determining the required system matrix coefficients is employed, in which a back-projection is performed in list-mode, and response coefficients determined for only tallied pixels. Projection data has been generated for a representative Compton camera system by Monte Carlo simulation for disk sources with hot and cold spots and energies of 141, 364, and 511 keV, and reconstructions performed.
Accelerated monotonic algorithms for transmission tomography

We present a framework for designing fast and monotonic algorithms for transmission tomography penalized likelihood image reconstruction. The new algorithms are based on paraboloidal surrogate functions for the log-likelihood. Due to the form of the log-likelihood function, it is possible to find low curvature surrogate functions that guarantee monotonicity. Unlike previous methods, the proposed surrogate functions lead to monotonic algorithms even for the nonconvex log-likelihood that arises due to background events such as scatter and random coincidences. The gradient and the curvature of the likelihood terms are evaluated only once per iteration. Since the problem is simplified, the CPU time per iteration is less than that of current algorithms which directly minimize the objective, yet the convergence rate is comparable. The simplicity, monotonicity and speed of the new algorithms are quite attractive. The convergence rates of the algorithms are demonstrated using real PET transmission scans.
Reconstruction of 3D whole-body PET data using blurred anatomical labels

The diagnostic utility of whole-body PET is often limited by the high level of statistical noise in the images. An improvement in image quality can be obtained by incorporating correlated anatomical information during the reconstruction of the PET data. The combined PET/CT (SMART) scanner allows the acquisition of accurately aligned PET and CT whole-body data. The authors present results of incorporating aligned anatomical information from the CT during the reconstruction of 3D whole-body PET data. They use the FORE+PWLS method for the reconstruction and a label model to incorporate anatomical information via penalty weights. Since in practice mismatches between anatomical and functional data are unavoidable, the labels are "blurred" to reflect the uncertainty associated with the anatomical information. Results show the potential advantage of incorporating anatomical information by using a blurred labels with the penalty weights.
MODEL-BASED MR IMAGE RECONSTRUCTION WITH COMPENSATION FOR THROUGH-PLANE FIELD INHOMOGENEITY

To improve image quality in susceptibility-weighted MR imaging, it is important to correct for the effects of field inhomogeneity. In particular, susceptibility differences between air and tissue induce magnetic field nonuniformity; often those susceptibility effects have nonzero through-plane gradients that lead to spin dephasing across the slice within each voxel. If uncorrected, these through-plane gradients cause signal loss in the reconstructed images. Several methods exist for reconstructing MR images with compensation for field inhomogeneity, but most of these methods, even the model-based iterative ones, treat the inhomogeneity within each voxel as being a constant, thus ignoring the through-plane gradient effects. This paper describes a model-based iterative method for reconstructing MR images with compensation for field inhomogeneity that accounts for the slice profile and the through-plane gradients of the field inhomogeneity (assumed to be determined by a pre-scan). In particular, this paper describes an accelerated algorithm for implementing the forward model and its adjoint as needed in a conjugate gradient algorithm for iterative MR image reconstruction.
Cross-validation and predicted risk estimation for nonlinear iterative reweighted least-squares MRI reconstruction

Regularization is an effective means of reducing noise and artifacts in MR image reconstruction from undersampled k-space data. Proper application of regularization demands appropriate selection of the associated regularization parameter. Generalized cross-validation (GCV) is a popular parameter tuning technique especially for linear reconstruction methods, but its application to nonlinear iterative MRI reconstruction is more involved as it demands the evaluation of the Jacobian matrix of the reconstruction algorithm with respect to complex-valued data. We derive analytical expressions for recursively updating this Jacobian matrix for an iterative reweighted least-squares reconstruction algorithm. Our method can also be used to calculate a predicted risk estimate (PSURE) for MRI based on Stein's principle. We demonstrate with simulations and experiments with real data that regularization parameter selection based on GCV and PSURE provides near-MSE-optimal results for nonlinear MRI reconstruction from undersampled k-space data using ℓ1-regularization.
Globally convergent ordered subsets algorithms: application to tomography

We present new algorithms for penalized-likelihood image reconstruction: modified BSREM (block sequential regularized expectation maximization) and relaxed OS-SPS (ordered subsets separable paraboloidal surrogates). Both of them are globally convergent to the unique solution, easily incorporate convex penalty functions, and are parallelizable-updating all voxels (or pixels) simultaneously. They belong to a class of relaxed ordered subsets algorithms. We modify the scaling function of the existing BSREM (De Pierro and Yamagishi, 2001) so that we can prove global convergence without previously imposed assumptions. We also introduce a diminishing relaxation parameter into the existing OS-SPS (Erdogan and Fessler, 1999) to achieve global convergence. We also modify the penalized-likelihood function to enable the algorithms to cover a zero-background-event case. Simulation results show that the algorithms are both globally convergent and fast.
Fast Large-Tip-Angle Multidimensional and Parallel RF Pulse Design in MRI

Large-tip-angle multidimensional radio-frequency (RF) pulse design is a difficult problem, due to the nonlinear response of magnetization to applied RF at large tip-angles. In parallel excitation, multidimensional RF pulse design is further complicated by the possibility for transmit field patterns to change between subjects, requiring pulses to be designed rapidly while a subject lies in the scanner. To accelerate pulse design, we introduce a fast version of the optimal control method for large-tip-angle parallel excitation. The new method is based on a novel approach to analytically linearizing the Bloch equation about a large-tip-angle RF pulse, which results in an approximate linear model for the perturbations created by adding a small-tip-angle pulse to a large-tip-angle pulse. The linear model can be evaluated rapidly using nonuniform fast Fourier transforms, and we apply it iteratively to produce a sequence of pulse updates that improve excitation accuracy. We achieve drastic reductions in design time and memory requirements compared to conventional optimal control, while producing pulses of similar accuracy. The new method can also compensate for nonidealities such as main field inhomogeneties.
Nonnegative definite quadratic penalty design for penalized-likelihood reconstruction

Likelihood-based estimators with conventional regularization methods generally produces images with nonuniform and anisotropic spatial resolution properties. Previous work on penalty design for penalized-likelihood estimators has led to statistical reconstruction methods that yield approximately uniform "average" resolution. However some asymmetries in the local point-spread functions persist. Such anisotropies result in the elongation of otherwise symmetric features like circular lesions. All previously published penalty functions have used nonnegative values for the weighting coefficients between neighboring voxels. Such nonnegativity provides a sufficient (but not necessary) condition to ensure that the penalty function is convex, which in turn ensures that the objective function has a unique maximizer. This paper describes a novel method for penalty design that allows a subset of the weighting coefficients to take negative values, while still ensuring convexity of the penalty function. We demonstrate that penalties designed under these more flexible constraints yield local point-spread functions that are more isotropic than the previous penalty design methods for 2D PET image reconstruction.
Bias-variance tradeoffs analysis using uniform CR bound for a SPECT system

The authors quantify fundamental bias-variance tradeoffs for the image reconstruction problem in radio-pharmaceutical tomography using Cramer-Rao (CR) bound analysis. The image reconstruction problem is very often biased and the classical or the unbiased CR bound on the mean square error performance of the estimator can not be used. The authors use a recently developed "uniform" CR bound which applies to biased estimators whose bias gradient satisfies a user specified length constraint. The authors demonstrate the use of the "uniform" CR bound for a simple SPECT system using several different examples.<<ETX>>
Estimating 3D respiratory motion from orbiting views

This paper describes a method for estimating 3D respiratory motion so as to characterize tumor motion. This method uses two sets of measurements. One is a reference thorax volume obtained from a conventional fast CT scanner under breath-hold condition. The other is a sequence of projection views of the same patient (acquired at treatment time) using a slowly rotating cone-beam system (1 minute per rotation) during free breathing. We named this method deformation from orbiting views (DOV). Breathing motion over the entire acquisition period is estimated by deforming the reference volume through time so that its projections best match the measured projection views. The nonrigid breathing motion is described by a B-spline based deformation model. The parameters of this model are estimated by minimizing a regularized squared error cost function, using a conjugate gradient descent algorithm. Performance of this approach was evaluated by simulation. Results showed good agreement between the estimated and synthesized motion, with a mean absolute error of 1.63 mm. Relatively larger errors tended to occur in uniform regions, which would not have significant effects on generating deformed volumes based on the estimated motion. The results indicate that it is feasible to estimate realistic nonrigid motion from a sequence of slowly rotating cone beam projection views.
Detector Blur and Correlated Noise Modeling for Digital Breast Tomosynthesis Reconstruction

This paper describes a new image reconstruction method for digital breast tomosynthesis (DBT). The new method incorporates detector blur into the forward model. The detector blur in DBT causes correlation in the measurement noise. By making a few approximations that are reasonable for breast imaging, we formulated a regularized quadratic optimization problem with a data-fit term that incorporates models for detector blur and correlated noise (DBCN). We derived a computationally efficient separable quadratic surrogate (SQS) algorithm to solve the optimization problem that has a non-diagonal noise covariance matrix. We evaluated the SQS-DBCN method by reconstructing DBT scans of breast phantoms and human subjects. The contrast-to-noise ratio and sharpness of microcalcifications were analyzed and compared with those by the simultaneous algebraic reconstruction technique. The quality of soft tissue lesions and parenchymal patterns was examined. The results demonstrate the potential to improve the image quality of reconstructed DBT images by incorporating the system physics model. This paper is a first step toward model-based iterative reconstruction for DBT.
Spoke pulse design in magnetic resonance imaging using greedy minimax algorithm

Spoke RF pulse design in MRI requires joint optimization of the k-space trajectory and RF pulse weights. This design task is often modelled as a sparse approximation problem with a cost function evaluating the l2 norm of the excitation error, which can be approximately solved using the orthogonal matching pursuit (OMP) algorithm. However, l2 optimization does not strictly regulate a maximum deviation between excitation and desired patterns, and may leave bright or dark spots in the image. In this paper, we model the pulse design problem as a sparse approximation problem with an l∞ norm cost function, and propose a greedy algorithm for solving this new problem. Simulation results demonstrate that our algorithm can produce improved spoke RF pulses (reduced maximum error) compared to l2 optimization.
Grouped-coordinate ascent algorithms for penalized-likelihood transmission image reconstruction

Presents a new class of algorithms for penalized-likelihood reconstruction of attenuation maps from low-count transmission scans. We derive the algorithms by applying to the transmission log-likelihood a version of the convexity technique developed by De Pierro for emission tomography. The new class includes the single-coordinate ascent (SCA) algorithm and Lange's convex algorithm for transmission tomography as special cases. The new grouped-coordinate ascent (GCA) algorithms in the class overcome several limitations associated with previous algorithms. (1) Fewer exponentiations are required than in the transmission maximum likelihood-expectation maximization (ML-EM) algorithm or in the SCA algorithm. (2) The algorithms intrinsically accommodate nonnegativity constraints, unlike many gradient-based methods. (3) The algorithms are easily parallelizable, unlike the SCA algorithm and perhaps line-search algorithms. We show that the GCA algorithms converge faster than the SCA algorithm, even on conventional workstations. An example from a low-count positron emission tomography (PET) transmission scan illustrates the method.
Comparison between post-smoothed maximum-likelihood and penalized-likelihood for image reconstruction with uniform spatial resolution

Regularization is desirable for image reconstruction in emission tomography. One of the most powerful regularization techniques is the penalized-likelihood reconstruction algorithm (or equivalently, maximum-a-posteriori reconstruction), where the sum of the likelihood and a noise suppressing penalty term (or Bayesian prior) is optimized. Usually, this approach yields position dependent resolution and bias. However, for some applications in emission tomography, a shift invariant point spread function would be advantageous. Recently, a new method has been proposed, in which the penalty term is tuned in every pixel in order to impose a uniform local impulse response. In this paper, an alternative way to tune the penalty term is presented. The performance of the new method is compared to that of the post-smoothed maximum-likelihood approach, using the impulse response of the former method as the post-smoothing filter for the latter. For this experiment, the noise properties of the penalized-likelihood algorithm were not superior to those of post-smoothed maximum-likelihood reconstruction.
Exact distribution of edge-preserving MAP estimators for linear signal models with Gaussian measurement noise

We derive the exact statistical distribution of maximum a posteriori (MAP) estimators having edge-preserving nonGaussian priors. Such estimators have been widely advocated for image restoration and reconstruction problems. Previous investigations of these image recovery methods have been primarily empirical; the distribution we derive enables theoretical analysis. The signal model is linear with Gaussian measurement noise. We assume that the energy function of the prior distribution is chosen to ensure a unimodal posterior distribution (for which convexity of the energy function is sufficient), and that the energy function satisfies a uniform Lipschitz regularity condition. The regularity conditions are sufficiently general to encompass popular priors such as the generalized Gaussian Markov random field prior and the Huber prior, even though those priors are not everywhere twice continuously differentiable.
Learning-Based Perceptual Image Quality Improvement for Video Conferencing

It is well known that in professional TV show filming, stage lighting has to be carefully designed in order to make the host and the scene look visually appealing. The lighting affects not only the brightness but also the color tone which plays a critical role in the perceived look of the host and the mood of the stage. In contrast, during video conferencing, the lighting is usually far from ideal thus the perceived image quality is low. There has been a lot of research on improving the brightness of the captured images. In this paper, we propose a learning-based technique to improve the perceptual image quality by enhancing both brightness and color tone. The basic idea is to learn the color statistics from a training set of images which look visually appealing, and adjust the color of an input image so that its color statistics matches those in the training set. To validate our approach, we have conducted user study and the results show that our technique significantly improves the perceived image quality.