nils-herrmann/clean_scirepeval huggingface dataset:
Joint estimation of image and deformation parameters in tomographic image reconstruction

We consider an emission tomography reconstruction problem in which projection measurements from several successive 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, parameters to describe the effects of motion are added to the statistical model of the projections. Joint maximum likelihood estimation of image and motion parameters is then carried out.
Recursive algorithms for computing the Cramer-Rao bound

Computation of the Cramer-Rao bound (CRB) on estimator variance requires the inverse or the pseudo-inverse Fisher information matrix (FIM). Direct matrix inversion can be computationally intractable when the number of unknown parameters is large. In this correspondence, we compare several iterative methods for approximating the CRB using matrix splitting and preconditioned conjugate gradient algorithms. For a large class of inverse problems, we show that nonmonotone Gauss-Seidel and preconditioned conjugate gradient algorithms require significantly fewer flops for convergence than monotone "bound preserving" algorithms.
Quantitative attenuation correction for PET/CT using iterative reconstruction of low-dose dual-energy CT

We present the results of using iterative reconstruction of dual-energy CT (DECT) to perform accurate CT-based attenuation correction (CTAC) for PET emission images. Current methods, such as bilinear scaling, introduce quantitative errors in the PET emission image for bone, metallic implants, and contrast agents. DECT has had limited use in the past for quantitative CT imaging due to increased patient dose and high noise levels in the decoupled CT basis-material images. Reconstruction methods that model the acquisition physics impose a significant computational burden due to the large image matrix size (typically 512 /spl times/ 512). For CTAC, however, three factors make DECT feasible: (1) a smaller matrix is needed for the transmission image, which reduces the noise per pixel, (2) a smaller matrix significantly accelerates an iterative CT reconstruction algorithm, (3) the monoenergetic transmission image at 511 keV is the sum of the two decoupled basis-material images. Initial results using a 128 /spl times/ 128 matrix size for a test object comprised of air, soft tissue, dense bone, and a mixture of tissue and bone demonstrate a significant reduction of bias using DECT (from 20% to /spl sim/0% for the tissue/bone mixture). FBP reconstructed images, however, have significant noise. Noise levels are reduced from /spl sim/8% to /spl sim/3% by the use of PWLS reconstruction.
An accelerated iterative reweighted least squares algorithm for compressed sensing MRI

Compressed sensing for MRI (CS-MRI) attempts to recover an object from undersampled k-space data by minimizing sparsity-promoting regularization criteria. The iterative reweighted least squares (IRLS) algorithm can perform the minimization task by solving iteration-dependent linear systems, recursively. However, this process can be slow as the associated linear system is often poorly conditioned for ill-posed problems. We propose a new scheme based on the matrix inversion lemma (MIL) to accelerate the solving process. We demonstrate numerically for CS-MRI that our method provides significant speed-up compared to linear and nonlinear conjugate gradient algorithms, thus making it a promising alternative for such applications.
Analytical approach to channelized Hotelling observer performance for regularized tomographic image reconstruction

Our goal is to analyze regularized image reconstruction methods such as penalized likelihood with respect to the performance of the channelized Hotelling observer (CHO) in the task of detecting a small target signal in the reconstructed images, in the presence of a correlated random background. We derive here an approximation to the performance of the CHO by working entirely with continuous-space formulations and then discretizing the final result. This approach leads to an extension and a refinement of approximations that we previously derived in the discrete space.
Joint estimation of image and fieldmap in parallel MRI using single-shot acquisitions

We propose a method for joint reconstruction of dynamic images and fieldmaps in parallel MRI, using single-shot trajectories. We exploit the sensitivity encoding from parallel imaging to reduce the length of acquisition and essentially perform joint reconstruction using just one full k-space dataset. We also explore the use of modified trajectories (both EPI and spiral) that provide full coverage of k-space and also contain enough inherent time differences to permit accurate fieldmap estimation. Finally we improve the efficiency of the reconstruction algorithm by using a linearization technique for fieldmap estimation, which allows the use of the conjugate gradient algorithm.
Recursive CR-Type Bounds and the EM Algorithm: Applications to ECT Image Reconstruction

We give a class of iterative algorithms to monotonically approximate submatrices of the CR matrix bound on the covariance of any estimator of a vector parameter/spl theta/. A natural implementation of the iterative algorithm employs a" complete data - incomplete data" formulation similar to that underlying the EM parameter estimation algorithm. Our results make it feasible to compute CR-type bounds for previously intractable problems involving a large number of "nuisance parameters," such as arise in image reconstruction.
Convergent incremental optimization transfer algorithms: application to tomography

No convergent ordered subsets (OS) type image reconstruction algorithms for transmission tomography have been proposed to date. In contrast, in emission tomography, there are two known families of convergent OS algorithms: methods that use relaxation parameters , and methods based on the incremental expectation-maximization (EM) approach . This paper generalizes the incremental EM approach by introducing a general framework, "incremental optimization transfer". The proposed algorithms accelerate convergence speeds and ensure global convergence without requiring relaxation parameters. The general optimization transfer framework allows the use of a very broad family of surrogate functions, enabling the development of new algorithms . This paper provides the first convergent OS-type algorithm for (nonconcave) penalized-likelihood (PL) transmission image reconstruction by using separable paraboloidal surrogates (SPS) which yield closed-form maximization steps. We found it is very effective to achieve fast convergence rates by starting with an OS algorithm with a large number of subsets and switching to the new "transmission incremental optimization transfer (TRIOT)" algorithm. Results show that TRIOT is faster in increasing the PL objective than nonincremental ordinary SPS and even OS-SPS yet is convergent.
Non-local means methods using CT side information for I-131 SPECT image reconstruction

Recently, non-local means (NLM) methods for both image denoising and inverse problems have shown promising results in image processing and medical imaging. Moreover, some researchers have also shown that using additional information with low noise and/or high resolution for these problems can improve the image quality further. We investigated several NLM methods including NLM filters and NLM regularizers with and without CT side information for 1-131 SPECT image reconstruction. We compared two different ways to incorporate CT side information in the NLM filtering methods. We also propose a new way of incorporating side information in the NLM reconstruction method. XCAT simulation results with two uniform tumors (72 cc, 11 cc) show that the NLM filtering method with CT side information decreased the root mean square error (RMSE) up to 18.6% as compared to unregularized method. Our proposed NLM-based regularizers in iterative image reconstructions with CT side information also yielded up to 29.2% better RMSE and up to 18.1 % better recovery coefficient than unregularized reconstruction.
Resolution properties in regularized dynamic MRI reconstruction

In dynamic MRI, one is constantly addressing the tradeoff between spatial and temporal resolution. Regularized reconstruction methods may offer benefits in terms of this tradeoff. However, selection of the regularization parameters is challenging. In this work we examine the spatial and temporal resolution of penalized-likelihood image reconstruction for dynamic MRI, and present an accelerated method for computing the local impulse response. This method may prove advantageous for regularization parameter selection.
Algorithms for joint estimation of attenuation and emission images in PET

In positron emission tomography (PET), positron emission from radiolabeled compounds yields two high energy photons emitted in opposing directions. However, often the photons are not detected due to attenuation within the patient. This attenuation is nonuniform and must be corrected to obtain quantitatively accurate emission images. To measure attenuation effects, one typically acquires a PET transmission scan before or after the injection of radiotracer. In commercially available 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 two-step reconstruction scheme does not use all the information in the transmission and emission scans. Postinjection transmission scans contain emission contamination that includes information about emission parameters. Similarly, emission scans contain information about the attenuating medium. To use all the available information, we propose a joint estimation approach that estimates the attenuation map and the emission image simultaneously from these two scans. The penalized-likelihood objective function is nonconvex for this problem. We propose an algorithm based on paraboloidal surrogates that alternates between updating emission and attenuation parameters and is guaranteed to monotonically decrease the objective function.
Emission image reconstruction for randoms-precorrected PET allowing negative sinogram values

Most positron emission tomography (PET) emission scans are corrected for accidental coincidence (AC) events by real-time subtraction of delayed-window coincidences, leaving only the randoms-precorrected data available for image reconstruction. The real-time randoms precorrection compensates in mean for AC events but destroys the Poisson statistics. The exact log-likelihood for randoms-precorrected data is inconvenient, so practical approximations are needed for maximum likelihood or penalized-likelihood image reconstruction. Conventional approximations involve setting negative sinogram values to zero, which can induce positive systematic biases, particularly for scans with low counts per ray. We propose new likelihood approximations that allow negative sinogram values without requiring zero-thresholding. With negative sinogram values, the log-likelihood functions can be nonconcave, complicating maximization; nevertheless, we develop monotonic algorithms for the new models by modifying the separable paraboloidal surrogates and the maximum-likelihood expectation-maximization (ML-EM) methods. These algorithms ascend to local maximizers of the objective function. Analysis and simulation results show that the new shifted Poisson (SP) model is nearly free of systematic bias yet keeps low variance. Despite its simpler implementation, the new SP performs comparably to the saddle-point model which has shown the best performance (as to systematic bias and variance) in randoms-precorrected PET emission reconstruction.
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction

The development of computed tomography (CT) image reconstruction methods that significantly reduce patient radiation exposure, while maintaining high image quality is an important area of research in low-dose CT imaging. We propose a new penalized weighted least squares (PWLS) reconstruction method that exploits regularization based on an efficient Union of Learned TRAnsforms (PWLS-ULTRA). The union of square transforms is pre-learned from numerous image patches extracted from a dataset of CT images or volumes. The proposed PWLS-based cost function is optimized by alternating between a CT image reconstruction step, and a sparse coding and clustering step. The CT image reconstruction step is accelerated by a relaxed linearized augmented Lagrangian method with ordered-subsets that reduces the number of forward and back projections. Simulations with 2-D and 3-D axial CT scans of the extended cardiac-torso phantom and 3-D helical chest and abdomen scans show that for both normal-dose and low-dose levels, the proposed method significantly improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer. PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform. We also incorporate patch-based weights in PWLS-ULTRA that enhance image quality and help improve image resolution uniformity. The proposed approach achieves comparable or better image quality compared to learned overcomplete synthesis dictionaries, but importantly, is much faster (computationally more efficient).
Reconstruction from digital holograms by statistical methods

Conventional numerical reconstruction for digital holography using a filter applied in the spatial frequency domain to extract the primary image may yield suboptimal image quality because of the loss in high-frequency components and interference from other undesirable terms of a hologram. In this paper, we propose a new numerical reconstruction approach using a statistical technique. This approach reconstructs the complex field of the object from the real-valued hologram intensity data. Because holographic image reconstruction is an ill-posed problem, our statistical technique is based on penalized-likelihood estimation. We develop a Poisson statistical model for this problem and derive an optimization transfer algorithm that monotonically decreases the cost function each iteration. Simulation results show that our statistical technique has the potential to improve image quality in digital holography relative to conventional reconstruction techniques.
A Splitting-Based Iterative Algorithm for Accelerated Statistical X-Ray CT Reconstruction

Statistical image reconstruction using penalized weighted least-squares (PWLS) criteria can improve image-quality in X-ray computed tomography (CT). However, the huge dynamic range of the statistical weights leads to a highly shift-variant inverse problem making it difficult to precondition and accelerate existing iterative algorithms that attack the statistical model directly. We propose to alleviate the problem by using a variable-splitting scheme that separates the shift-variant and ("nearly") invariant components of the statistical data model and also decouples the regularization term. This leads to an equivalent constrained problem that we tackle using the classical method-of-multipliers framework with alternating minimization. The specific form of our splitting yields an alternating direction method of multipliers (ADMM) algorithm with an inner-step involving a "nearly" shift-invariant linear system that is suitable for FFT-based preconditioning using cone-type filters. The proposed method can efficiently handle a variety of convex regularization criteria including smooth edge-preserving regularizers and non- smooth sparsity-promoting ones based on the ℓ1-norm and total variation. Numerical experiments with synthetic and real in vivo human data illustrate that cone-filter preconditioners accelerate the proposed ADMM resulting in fast convergence of ADMM compared to conventional (nonlinear conjugate gradient, ordered subsets) and state-of-the-art (MFISTA, split-Bregman) algorithms that are applicable for CT.
Regularized Estimation of Magnitude and Phase of Multi-Coil $B_1$ Field Via Bloch–Siegert $B_1$  Mapping and Coil Combination Optimizations

Parallel excitation requires fast and accurate B1 map estimation. Bloch-Siegert (BS) B1 mapping is very fast and accurate over a large dynamic range. When applied to multi-coil systems, however, this phase-based method may produce low signal-to-noise ratio estimates in low magnitude regions due to localized excitation patterns of parallel excitation systems. Also, the imaging time increases with the number of coils. In this work, we first propose to modify the standard BS B1 mapping sequence so that it avoids the scans required by previous B1 phase estimation methods. A regularized method is then proposed to jointly estimate the magnitude and phase of multi-coil B1 maps from BS B1 mapping data, improving estimation quality by using the prior knowledge of the smoothness of B1 magnitude and phase. Lastly, we use Cramer-Rao lower bound analysis to optimize the coil combinations, to improve the quality of the raw data for B1 estimation. The proposed methods are demonstrated by simulations and phantom experiments.
Statistical X-ray CT reconstruction using a splitting-based iterative algorithm with orthonormal wavelets

Regularized image reconstruction methods are of increasing interest for X-ray CT imaging, but are hampered by the long computation times of iterative algorithms. We recently developed a variable splitting-based alternating direction method of multipliers (ADMM) that provides superior convergence speeds for statistical X-ray CT reconstruction compared to conventional methods. ADMM however demands storing auxiliary constraint variables and can become memory-expensive when shift-invariant regularization operators (e.g., finite differences) are used, especially in 3-D CT. Since an orthonormal wavelet transform (OWT) is memory-efficient, in this work, we employ OWT with nonquadratic regularization in ADMM for CT reconstruction. We propose a practical strategy for performing iteration-dependent random shifting in ADMM to (partially) compensate for the shift-variance of OWT and reduce block-artifacts therefrom. Preliminary evaluations with a 2-D synthetic phantom and real 2-D in-vivo human head data indicate that the proposed strategy provides CT reconstructions that are comparable in quality to those obtained using nonquadratic regularization with finite differences.
Model-based 3-D reconstruction of branching vessels

An approach to the problem of reconstructing a 3-D arterial tree model from a few angiographic projections is described. A natural optimality criterion that defines the globally best reconstruction is defined, and then an algorithm that maximizes this criterion is outlined. The criterion is a compromise between the conflicting goals of measurement likelihood and object smoothness. The approach uses a parametric model for the arterial tree, with a more accurate model for bifurcations. The algorithm is demonstrated on simulated projections and on magnetic resonance (MR) images.<<ETX>>
Incorporation of system resolution compensation (RC) in the ordered-subset transmission (OSTR) algorithm for transmission imaging in SPECT

In order to reconstruct attenuation maps with improved spatial resolution and quantitative accuracy, we developed an approximate method of incorporating system resolution compensation (RC) in the ordered-subset transmission (OSTR) algorithm for transmission reconstruction. Our method approximately models the blur caused by the finite intrinsic detector resolution, the nonideal source collimation and detector collimation. We derived the formulation using the optimization transfer principle as in the derivation of the OSTR algorithm. The formulation includes one forward-blur step and one back-blur step, which do not severely slow down reconstruction. The formulation could be applicable to various transmission geometries, such as point-source, line-source, and sheet-source systems. Through computer simulations of the MCAT phantom and transmission measurements of the air-filled Data Spectrum Deluxe single photo emission computed tomography (SPECT) Phantom on a system which employed a cone-beam geometry and a system which employed a scanning-line-source geometry, we showed that incorporation of RC increased spatial resolution and improved the quantitative accuracy of reconstruction. In simulation studies, attenuation maps reconstructed with RC correction improved the quantitative accuracy of emission reconstruction
Mean and variance of photon counting with deadtime [in PET and SPECT imaging]

The statistics of photon counting by systems affected by deadtime are potentially important for statistical image reconstruction methods. We present a new way of analyzing the moments of the counting process for a counter system affected by various models of deadtime related to PET and SPECT imaging. We derive simple and exact expressions for the first and second moments of the number of recorded events under various models. From our mean expression for a SPECT deadtime model, we derive a simple estimator for the actual intensity of the underlying Poisson process; simulations show that our estimator is unbiased even for extremely high count rates.
Nonparametric fixed-interval smoothing of nonlinear vector-valued measurements

The problem of estimating a smooth vector-valued function given noisy nonlinear vector-valued measurements of that function is addressed. A nonparametric optimality criterion for this estimation problem is presented, and a computationally efficient iterative algorithm for its solution is developed. The criterion is the natural generalization of previously published work on vector splines with linear measurement models. The algorithm provides an alternative to the extended Kalman filter, as it does not require a parametric state-space model. An automatic procedure that uses the measurements to determine how much to smooth is presented. The algorithm's subpixel estimation accuracy is demonstrated on the estimation of a curved edge in a noisy image and on a biomedical image-processing application.
Analysis of observer performance in known-location tasks for tomographic image reconstruction

We consider the task of detecting a statistically varying signal of known location on a statistically varying background in a reconstructed tomographic image. We analyze the performance of linear observer models in this task. We show that, if one chooses a suitable reconstruction method, a broad family of linear observers can exactly achieve the optimal detection performance attainable with any combination of a linear observer and linear reconstructor. This conclusion encompasses several well-known observer models from the literature, including models with a frequency-selective channel mechanism and certain types of internal noise. Interestingly, the "optimal" reconstruction methods are unregularized and in some cases quite unconventional. These results suggest that, for the purposes of designing regularized reconstruction methods that optimize lesion detectability, known-location tasks are of limited use.