nils-herrmann/clean_scirepeval huggingface dataset:
3D PET reconstruction with FORE and WLS-OS-EM

2D PET reconstructions by the ordered subsets version of the ML-EM algorithm (typically referred to as "OS-EM") assume that the random- and scattered-corrected coincidence data retain their Poisson statistics. Negative projection values, which are possible after these subtractive corrections, are either truncated at zero counts or accommodated by adding an offset to the projection data, and to the corresponding forward-projected values, during the algorithm. These methods are effective due to the relatively low scatter and random fractions of most 2D PET acquisitions. In 3D PET data, particularly in acquisitions in the body, the scatter and random fractions are much higher. As a result, the OS-EM algorithm may not be the most appropriate for reconstruction for rebinned 3D PET data. We have implemented an ordered-subsets version of the weighted least-squares expectation maximization (WLS-EM) algorithm, or WLS-OS-EM, which is based on a Gaussian approximation to the statistics of the rebinned data. The weights used in the algorithm are based on the attenuation correction factors applied to the data. WLS-OS-EM exhibits faster, and more consistent, convergence than ML-OS-EM for projection data sets with substantial negative projection values created by subtractive data corrections.
Fast parallelizable algorithms for transmission image reconstruction

Presents a new class of algorithm for penalized-likelihood reconstruction of attenuation maps from low-count transmission scans. The authors derive the algorithms by applying to the transmission log-likelihood a variation of the convexity technique developed by De Pierro for the emission case. The new algorithms overcome several limitations associated with previous algorithms. (1) Fewer exponentiations are required than in the transmission EM algorithm or in coordinate-ascent algorithms. (2) The algorithms intrinsically accommodate nonnegativity constraints, unlike many gradient-based methods. (3) The algorithms are easily parallelizable, unlike coordinate-ascent algorithms and perhaps line-search algorithms. The authors show that the algorithms converge faster than several alternatives, even on conventional workstations. They give examples from low-count PET transmission scans and from truncated fan-beam SPECT transmission scans.
Hidden-data spaces for maximum-likelihood PET reconstruction

The author shows that expectation-maximization (EM) algorithms based on smaller complete data spaces will typically converge faster. As an example, he compares the two maximum-likelihood (ML) image reconstruction algorithms of D. G. Politte and D. L. Snyder (1991) which are based on measurement models that account for attenuation and accidental coincidences in positron-emission tomography (PET).<<ETX>>
Joint maximum likelihood estimation of emission and attenuation densities in PET

Accurate attenuation correction can be performed in PET (positron emission tomography) using transmission scanning to estimate the survival probabilities along each coincidence line. However, since these measurements are typically corrupted by Poisson counting noise, they propagate additional uncertainty into reconstructed images and kinetic parameter estimates. This can be especially true in the thorax where the attenuating medium is heterogeneous and the statistical precision of the transmission scan may be approximately the same as that of the emission data. To account for the Poisson noise in the transmission measurement, the authors have developed a sieve-constrained maximum likelihood algorithm that jointly estimates both the survival probability and the emission intensity. They present some of their initial experiences in using the joint alternate and maximize algorithm with simulated PET data.<<ETX>>
Joint estimation for incorporating MRI anatomic images into SPECT reconstruction

To improve SPECT reconstruction using spatially-correlated magnetic resonance (MR) images as a source of side information, one must account for mismatch between MRI anatomical information and SPECT functional information. The authors investigate an approach which incorporates the anatomical information into SPECT reconstruction by using region labels representing the anatomical regions extracted from MRI. Each SPECT pixel corresponds to one region label. Both SPECT pixel mean intensities and region labels are jointly estimated by a penalized Maximum-Likelihood criterion using an iterative Space-Alternating Generalized EM algorithm. The likelihood function incorporates both the SPECT noise distribution and the MRI side information measurement statistics. Since the region labels are estimated jointly from both segmented MRI and SPECT projection data, only those anatomical regions that match SPECT functional regions are represented by the estimated labels, and are used to constrain the SPECT reconstruction. The artifacts due to the mismatched MR anatomical region information are reduced using joint estimation. By comparing image quality and the Bias vs. Variance tradeoffs, the authors see that the joint estimation has the potential to improve the SPECT estimation result.<<ETX>>
Fast kVp-switching dual energy CT for PET attenuation correction

X-ray CT images are used routinely for attenuation correction in PET/CT systems. However, conventional CT-based attenuation correction (CTAC) can be inaccurate in regions containing iodine contrast agent. Dual-energy (DE) CT has the potential to improve the accuracy of attenuation correction in PET, but conventional DECT can suffer from motion artifacts. Recent X-ray CT systems can collect DE sinograms by rapidly switching the X-ray tube voltage between two levels for alternate projection views, reducing motion artifacts. The goal of this work is to study statistical methods for image reconstruction from both fast kVp-switching DE scans and from conventional dual-rotate DE scans in the context of CTAC for PET.
Spatially-variant roughness penalty design for uniform resolution in penalized-likelihood image reconstruction

Traditional space-invariant regularization schemes in tomographic image reconstruction using penalized likelihood estimators produce images with nonuniform resolution properties. The local point spread functions that quantify the local smoothing properties of such estimators are not only space-variant and asymmetric, but are also object-dependent even for space-invariant systems. We propose a new regularization scheme for increased spatial uniformity and demonstrate the resolution properties of this new method versus conventional regularization schemes through an investigation of local point spread functions.
Bias-variance trade-offs analysis using uniform CR bound for images

We apply a uniform Cramer-Rao (CR) bound to study the bias-variance trade-offs in parameter estimation. The uniform CR bound is used to specify achievable and unachievable regions in the bias-variance trade-off plane. The applications considered are: (1) two-dimensional single photon emission computed tomography (SPECT) system, and (2) one dimensional edge localization.<<ETX>>
Edge-preserving tomographic reconstruction with nonlocal regularization

Tomographic image reconstruction using statistical methods can provide more accurate system modeling, statistical models, and physical constraints than the conventional filtered backprojection (FBP) method. Because of the ill posedness of the reconstruction problem, a roughness penalty is often imposed on the solution to control noise. To avoid smoothing of edges, which are important image attributes, various edge-preserving regularization methods have been proposed. Most of these schemes rely on information from local neighborhoods to determine the presence of edges. In this paper, we propose a cost function that incorporates nonlocal boundary information into the regularization method. We use an alternating minimization algorithm with deterministic annealing to minimize the proposed cost function, jointly estimating region boundaries and object pixel values. We apply variational techniques implemented using level-sets methods to update the boundary estimates; then, using the most recent boundary estimate, we minimize a space-variant quadratic cost function to update the image estimate. For the positron emission tomography transmission reconstruction application, we compare the bias-variance tradeoff of this method with that of a "conventional" penalized-likelihood algorithm with local Huber roughness penalty.
Uniform CR bound: implementation issues and applications

The authors apply a uniform Cramer-Rao (CR) bound (A.O. Hero, 1992) to study the bias-variance trade-offs in single photon emission computed tomography (SPECT) image reconstruction. The uniform CR bound is used to specify achievable and unachievable regions in the bias-variance trade-off plane. The image reconstruction algorithms considered here are: 1) space alternating generalized EM and 2) penalized weighted least-squares.<<ETX>>
Ordered subsets with momentum for accelerated X-ray CT image reconstruction

Statistical image reconstruction methods provide improved image quality in low-dose X-ray CT. However, the long computation time of iterative algorithms limits their clinical use. Ordered subsets algorithms based on separable quadratic surrogates (OS-SQS) are attractive as they are simple and amenable for massive parallelization in modern computing architecture, but require many iterations to converge. Here, we further accelerate OS algorithms by using momentum techniques. We use real patient CT scan to illustrate that the proposed algorithms converge rapidly compared to previous OS algorithms.
Approximate variance images for penalized-likelihood image reconstruction

The authors present a fairly simple procedure for computing new approximations for the pixel variances in images reconstructed by penalized-likelihood methods. The method enables the display of variance images, which can provide an indication of uncertainty that may be helpful in medical diagnosis and in evaluation of image reconstruction algorithms. Simulations of positron emission tomography (PET) scans illustrate the accuracy of the proposed variance approximations in nonzero image pixels.
Incorporating MRI region information into SPECT reconstruction using joint estimation

Single photon emission computed tomographic images (SPECT) have relatively poor resolution. In an attempt to improve SPECT image quality, many methods have been developed for including anatomic information, extracted from higher resolution, structurally correlated magnetic resonance images (MRI), into the SPECT reconstruction process. These methods provide improved SPECT reconstruction accuracy if the anatomic information is perfectly correlated with the SPECT functional information. However there exist mismatches between MRI anatomical structures and SPECT functional structures due to different imaging mechanisms. It has been reported that if the MR structures are applied into SPECT, the mismatched part will cause artifacts. The paper describes a joint estimation approach which unifies MR information extraction and SPECT reconstruction processes to avoid these artifacts. Both qualitative and quantitative evaluations show that the method improves the SPECT reconstruction where the MR information matches and is robust to mismatched MR information.
Joint estimation of image and deformation parameters in motion-corrected PET

We consider an emission tomography reconstruction problem in which projection measurements from several time frames are available. Two strategies for doing motion-corrected image reconstruction are compared. In the first strategy, separate images are reconstructed from the measurements at each time frame. They are then consolidated by post-registration and averaging procedures. In the second strategy, we incorporate parameters to describe the effects of motion into the statistical model of the projections. Joint maximum likelihood estimation of image and motion parameters is then carried out. Each of these strategies involves the minimization of non-convex cost functions. Accordingly, we also propose some relevant optimization algorithm design options.
Fourier-based forward and back-projectors in iterative fan-beam tomographic image reconstruction

Fourier-based forward and back-projection methods can reduce computation in iterative tomographic image reconstruction. Recently, an optimized nonuniform fast Fourier transform (NUFFT) approach was shown to yield accurate parallel-beam projections. In this paper, we extend the NUFFT approach to describe an O(N/sup 2/ log N) projector/backprojector pair for fan-beam transmission tomography. Simulations and experiments with real CT data show that fan-beam Fourier-based forward and back-projection methods can reduce computation for iterative reconstruction while still providing accuracy comparable to their O(N/sup 3/) space-based counterparts.
Analysis of unknown-location signal detectability for regularized tomographic image reconstruction

Our goal is to optimize regularized image reconstruction methods for emission tomography with respect to the task of detecting small lesions of unknown location in the reconstructed images. We consider model observers whose decisions are based on finding the maximum value of a local test statistic over all possible lesion locations. We use tail probability approximations by Adler (AAP 2000) and Siegmund and Worsley (AS 1995) to evaluate the probabilities of false alarm and detection respectively for the observers of interest. We illustrate how these analytical tools can be used to optimize regularization with respect to the performance (at low probability of false alarm operating points) of a maximum channelized non-prewhitening observer
Model-based estimation for dynamic cardiac studies using ECT

The authors develop a strategy for joint estimation of physiological parameters and myocardial boundaries using ECT (emission computed tomography). They construct an observation model to relate parameters of interest to the projection data and to account for limited ECT system resolution and measurement noise. The authors then use a maximum likelihood (ML) estimator to jointly estimate all the parameters directly from the projection data without reconstruction of intermediate images. They also simulate myocardial perfusion studies based on a simplified heart model to evaluate the performance of the model-based joint ML estimator and compare this performance to the Cramer-Rao lower bound. Finally, the authors discuss model assumptions and potential uses of the joint estimation strategy.
Near-optimal signal detection for finite-state Markov signals with application to magnetic resonance force microscopy

Detection of a finite-state Markov signal in additive white Gaussian noise (AWGN) can be done in an intuitive manner by applying an appropriate filter and using an energy detector. One might not expect this heuristic approach to signal detection to be optimal. However, in this paper, we show that for a certain type of finite-state Markov signal, namely, the discrete-time (DT) random telegraph, this filtered energy detector is approximately optimal under the following conditions of: symmetric transition probabilities, low signal-to-noise ratio (SNR), long observation time, and small probability of transition between two consecutive time instances. When these last three conditions hold, but the transition probabilities are not symmetric, we show that a variant of the filtered energy detector is approximately optimal. It is also shown, under low SNR conditions, that the likelihood ratio test (LRT) for a finite-state DT Markov signal in AWGN reduces to the matched filter statistic with the minimum mean-squared-error (MMSE) predictor signal values used in place of the known signal values. Using this result, we propose a general methodology for obtaining an approximation to the LRT of a finite-state DT Markov signal in AWGN. Specifically, instead of the conditional mean (also MMSE) estimators, affine estimators with lowest mean squared error are used. This work is relevant to magnetic resonance force microscopy, an emerging technology that uses ultrasensitive force sensing to detect magnetic resonance. Sensitivity down to the single spin level was demonstrated in a recent experiment.
Covariance of kinetic parameter estimators based on time activity curve reconstructions: preliminary study on 1D dynamic imaging

We provide approximate expressions for the covariance matrix of kinetic parameter estimators based on time activity curve (TAC) reconstructions when TACs are modeled as a linear combination of temporal basis functions such as B-splines. The approximations are useful tools for assessing and optimizing the basis functions for TACs and the temporal bins for data in terms of computation and efficiency. In this paper we analyze a 1D temporal problem for simplicity, and we consider a scenario where TACs are reconstructed by penalized-likelihood (PL) estimation incorporating temporal regularization, and kinetic parameters are obtained by maximum likelihood (ML) estimation. We derive approximate formulas for the covariance of the kinetic parameter estimators using 1) the mean and variance approximations for PL estimators in (Fessler, 1996) and 2) Cramer-Rao bounds. The approximations apply to list-mode data as well as bin-mode data.
Discontinuity preserving regularization for modeling sliding in medical image registration

Sliding effects often occur along tissue/organ boundaries. However, most conventional registration techniques either use smooth parametric bases or apply homogeneous smoothness regularization, and fail to address the sliding issue. In this study, we propose a class of discontinuity-preserving regularizers that fit naturally into optimization-based registration. The proposed regularization encourages smooth deformations in most regions, but preserves large discontinuities supported by the data. Variational techniques are used to derive the descending flows. We discuss general conditions on such discontinuity-preserving regularizers, and their properties based on an anisotropic filtering interpretation. Preliminary tests with 2D CT data show promising results.
Regularization for uniform spatial resolution properties in penalized-likelihood image reconstruction

Traditional space-invariant regularization methods in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform spatial resolution properties. The local point spread functions that quantify the smoothing properties of such estimators are space variant, asymmetric, and object-dependent even for space invariant imaging systems. The authors propose a new quadratic regularization scheme for tomographic imaging systems that yields increased spatial uniformity and is motivated by the least-squares fitting of a parameterized local impulse response to a desired global response. The authors have developed computationally efficient methods for PET systems with shift-invariant geometric responses. They demonstrate the increased spatial uniformity of this new method versus conventional quadratic regularization schemes in simulated PET thorax scans.
Noise Properties of Motion-Compensated Tomographic Image Reconstruction Methods

Motion-compensated image reconstruction (MCIR) methods incorporate motion models to improve image quality in the presence of motion. MCIR methods differ in terms of how they use motion information and they have been well studied separately. However, there have been less theoretical comparisions of different MCIR methods. This paper compares the theoretical noise properties of three popular MCIR methods assuming known nonrigid motion. We show the relationship among three MCIR methods-motion-compensated temporal regularization (MTR), the parametric motion model (PMM), and post-reconstruction motion correction (PMC)-for penalized weighted least square cases. These analyses show that PMM and MTR are matrix-weighted sums of all registered image frames, while PMC is a scalar-weighted sum. We further investigate the noise properties of MCIR methods with Poisson models and quadratic regularizers by deriving accurate and fast variance prediction formulas using an “analytical approach.” These theoretical noise analyses show that the variances of PMM and MTR are lower than or comparable to the variance of PMC due to the statistical weighting. These analyses also facilitate comparisons of the noise properties of different MCIR methods, including the effects of different quadratic regularizers, the influence of the motion through its Jacobian determinant, and the effect of assuming that total activity is preserved. Two-dimensional positron emission tomography simulations demonstrate the theoretical results.
Model-based estimation with boundary side information or boundary regularization [cardiac emission CT]

The authors have previously developed a model-based strategy for joint estimation of myocardial perfusion and boundaries using ECT (emission computed tomography). They have also reported difficulties with boundary estimation in low contrast and low count rate situations. Here they propose using boundary side information (obtainable from high resolution MRI and CT images) or boundary regularization to improve both perfusion and boundary estimation in these situations. To fuse boundary side information into the emission measurements, the authors formulate a joint log-likelihood function to include auxiliary boundary measurements as well as ECT projection measurements. In addition, they introduce registration parameters to align auxiliary boundary measurements with ECT measurements and jointly estimate these parameters with other parameters of interest from the composite measurements. In simulated PET O-15 water myocardial perfusion studies using a simplified model, the authors show that the joint estimation improves perfusion estimation performance and gives boundary alignment accuracy of <0.5 mm even at 0.2 million counts. They implement boundary regularization through formulating a penalized log-likelihood function. They also demonstrate in simulations that simultaneous regularization of the epicardial boundary and myocardial thickness gives comparable perfusion estimation accuracy with the use of boundary side information.
Edge-Preserving Image Denoising via Group Coordinate Descent on the GPU

Image denoising is a fundamental operation in image processing, and its applications range from the direct (photographic enhancement) to the technical (as a subproblem in image reconstruction algorithms). In many applications, the number of pixels has continued to grow, while the serial execution speed of computational hardware has begun to stall. New image processing algorithms must exploit the power offered by massively parallel architectures like graphics processing units (GPUs). This paper describes a family of image denoising algorithms well-suited to the GPU. The algorithms iteratively perform a set of independent, parallel 1D pixel-update subproblems. To match GPU memory limitations, they perform these pixel updates in-place and only store the noisy data, denoised image, and problem parameters. The algorithms can handle a wide range of edge-preserving roughness penalties, including differentiable convex penalties and anisotropic total variation. Both algorithms use the majorize-minimize framework to solve the 1D pixel update subproblem. Results from a large 2D image denoising problem and a 3D medical imaging denoising problem demonstrate that the proposed algorithms converge rapidly in terms of both iteration and run-time.