nils-herrmann/clean_scirepeval huggingface dataset:
Model-based image reconstruction for dual-energy X-ray CT with fast KVP switching

The most recent generation of X-ray CT systems can collect dual energy (DE) sinograms by rapidly switching the X-ray tube voltage between two levels for alternate projection views. This reduces motion artifacts in DE imaging, but yields sinograms that may be angularly under-sampled. This paper describes an iterative algorithm for statistical image reconstruction of material component images (e.g., soft tissue and bone) directly from such under-sampled DE data, without resorting to the interpolation operations required by conventional DE reconstruction methods.
Dynamic MR image and fieldmap joint reconstruction accounting for through-plane fieldmap gradients

In susceptibility-weighted MRI, ignoring the magnetic field inhomogeneity can lead to severe reconstruction artifacts. Correcting for the effects of magnetic field inhomogeneity requires accurate fieldmaps. Especially in functional MRI, dynamic updates are desirable, since the fieldmap may change in time. Also, susceptibility effects that induce field inhomogeneity often have non-zero throughplane gradients, which, if uncorrected, can cause signal loss in the reconstructed images. Most image reconstruction methods that compensate for field inhomogeneity, even using dynamic fieldmap updates, ignore through-plane fieldmap gradients. This work proposes a computationally efficient, model based iterative method for joint reconstruction of image and dynamic fieldmap that accounts for the through-plane gradients of the field inhomogeneity. The proposed method allows for efficient reconstruction by applying fast approximations that allow the use of the conjugate gradient algorithm along with FFTs.
A penalized-likelihood image reconstruction method for emission tomography, compared to postsmoothed maximum-likelihood with matched spatial resolution

Regularization is desirable for image reconstruction in emission tomography. A powerful regularization method is the penalized-likelihood (PL) 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 to impose a uniform local impulse response. In this paper, an alternative way to tune the penalty term is presented. We performed positron emission tomography and single photon emission computed tomography simulations to compare the performance of the new method to that of the postsmoothed maximum-likelihood (ML) approach, using the impulse response of the former method as the postsmoothing filter for the latter. For this experiment, the noise properties of the PL algorithm were not superior to those of postsmoothed ML reconstruction.
Comments on "The convergence of mean field procedures for MRF's"

The author comments that Zhang (see ibid., vol.5, no.12, p.1662-65, 1996) attempts to establish the convergence of a mean field iteration for an Ising Markov random field (MRF) for large values of the hyperparameter value /spl beta/ is not correct for the function T/sub /spl delta//(u) defined. The convergence of the mean field iteration remains an open question for large /spl beta/.
Regularized methods for topology-preserving smooth nonrigid image registration using B-spline basis

B-splines are a convenient tool for nonrigid registration, but ensuring invertibility can be challenge. This paper describes a new penalty method that is devised to enforce a sufficient condition for local invertibility and smoothness of nth order B-spline based deformations. Traditional direct Jacobian penalty methods penalize negative Jacobian determinant values only at grid points. In contrast, our new penalty method enforces the sufficient condition for invertibility directly on the B-spline coefficients by using a modified quadratic penalty function so that it enforces invertibility globally over a 3D continuous domain. This approach also saves computation time and memory compared to using Jacobian determinant values. We apply this method to 3D CT images of a thorax at inhale and exhale.
Fast methods for approximation of resolution and covariance for SPECT

Resolution and covariance predictors have been derived previously for penalized-likelihood estimators. These predictors can provide accurate approximations to the local resolution properties and covariance functions for tomographic systems given a good estimate of the mean measurements. However, when numerous evaluations are made repeatedly (as in penalty design or calculation of variance images), these predictors still require large amounts of computing time. In, we discussed methods for precomputing a large portion of the predictor for shift-invariant system geometries. In this paper, we generalize the efficient procedure discussed in to shift-variant single photon emission computed tomography (SPECT) systems. This generalization relies on a new attenuation approximation and several observations on the symmetries in SPECT systems. These new general procedures apply to both 2D and fully-3D SPECT models, that may be either precomputed and stored, or written in procedural form.
Alternating minimization approach for multi-frame image reconstruction

There are a variety of imaging modalities that record a sequence of measurements where the sensor and/or the objects in the scene are moving and the goal is to reconstruct an image without motion blur. Examples include multi-frame super-resolution problems and motion-compensated image reconstruction problems in medical imaging. Various methods have been proposed for such applications, often in the context of specific imaging modalities. However, many such methods can be formulated in a common framework and thus solved by the same optimization method. To solve the reconstruction problem efficiently, the optimization method must be designed carefully. This paper proposes a novel approach to solve multi-frame image reconstruction problems more efficiently. We use a variable-splitting technique to dissociate the original problem into a few simpler problems that are then solved individually using an alternating minimization method. The proposed method is amenable to preconditioning, parallelization, and application of block iterative algorithms to the sub-problems. Simulation results demonstrate that even with simple diagonal or circulant preconditioners, the proposed method converges faster than the conjugate gradient (CG) method.
Image restoration using non-circulant shift-invariant system models

Image restoration is a well studied problem and there are several proposed methods for deblurring and denoising. Recently, there is increasing interest in iterative schemes that employ non-quadratic regularizers, especially edge-preserving like Total Variation (TV) and sparsity promoting like l1 regularization. Most methods make simplifying assumptions concerning the system model and the most common one is the use of a circulant blurring model because it facilitates using the FFT. In this work we focus on a more realistic non-circulant blurring model and apply existing algorithms for image restoration with non-quadratic regularization, tailored to work with our non-circulant model.
Intensity-based image registration using robust correlation coefficients

The ordinary sample correlation coefficient is a popular similarity measure for aligning images from the same or similar modalities. However, this measure can be sensitive to the presence of "outlier" objects that appear in one image but not the other, such as surgical instruments, the patient table, etc., which can lead to biased registrations. This paper describes an intensity-based image registration technique that uses a robust correlation coefficient as a similarity measure. Relative to the ordinary sample correlation coefficient, the proposed similarity measure reduces the influence of outliers. We also compared the performance of the proposed method with the mutual information-based method. The robust correlation-based method should be useful for image registration in radiotherapy (KeV to MeV X-ray images) and image-guided surgery applications. We have investigated the properties of the proposed method by theoretical analysis, computer simulations, a phantom experiment, and with functional magnetic resonance imaging (MRI) data.
Benefits of Position-Sensitive Detectors for Radioactive Source Detection

There are many systems for counting photons such as gamma-rays emitted from radioactive sources. Many of these systems are also position-sensitive, which means that the system provides directional information about recorded events. This paper investigates whether or not the additional information provided by position-sensitive capability improves the performance of detecting a point-source in background. We analyze the asymptotic performance of the generalized likelihood ratio test (GLRT) and a test based on the maximum-likelihood (ML) estimate of the source intensity for systems with and without position-sensitive capability. When the background intensity is known and detector sensitivity is spatially uniform, we prove that position-sensitive capability increases the area under the receiver operating characteristic curve (AUC). For cases when detector sensitivity is nonuniform or background intensity is unknown, we provide numerical results to illustrate the effect of the parameters on detection performance.
A Simple Regularizer for B-spline Nonrigid Image Registration That Encourages Local Invertibility

Nonrigid image registration is an important task for many medical imaging applications. In particular, for radiation oncology it is desirable to track respiratory motion for thoracic cancer treatment. B-splines are convenient for modeling nonrigid deformations, but ensuring invertibility can be a challenge. This paper describes sufficient conditions for local invertibility of deformations based on B-spline bases. These sufficient conditions can be used with constrained optimization to enforce local invertibility. We also incorporate these conditions into nonrigid image registration methods based on a simple penalty approach that encourages diffeomorphic deformations. Traditional Jacobian penalty methods penalize negative Jacobian determinant values only at grid points. In contrast, our new method enforces a sufficient condition for invertibility directly on the deformation coefficients to encourage invertibility globally over a 3-D continuous domain. The proposed penalty approach requires substantially less compute time than Jacobian penalties per iteration.
Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction.

Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality. However, the longer reconstruction time of SIR methods hinders their use in X-ray CT in practice. To accelerate statistical methods, many optimization techniques have been investigated. Over-relaxation is a common technique to speed up convergence of iterative algorithms. For instance, using a relaxation parameter that is close to two in alternating direction method of multipliers (ADMM) has been shown to speed up convergence significantly. This paper proposes a relaxed linearized augmented Lagrangian (AL) method that shows theoretical faster convergence rate with over-relaxation and applies the proposed relaxed linearized AL method to X-ray CT image reconstruction problems. Experimental results with both simulated and real CT scan data show that the proposed relaxed algorithm (with ordered-subsets [OS] acceleration) is about twice as fast as the existing unrelaxed fast algorithms, with negligible computation and memory overhead.
Parallel MR Image Reconstruction Using Augmented Lagrangian Methods

Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity-based regularization criteria can improve image quality, but they demand computation-intensive nonlinear optimization. In this paper, we present novel methods for regularized MRI reconstruction from undersampled sensitivity encoded data-SENSE-reconstruction-using the augmented Lagrangian (AL) framework for solving large-scale constrained optimization problems. We first formulate regularized SENSE-reconstruction as an unconstrained optimization task and then convert it to a set of (equivalent) constrained problems using variable splitting. We then attack these constrained versions in an AL framework using an alternating minimization method, leading to algorithms that can be implemented easily. The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., -norm of wavelet coefficients) criteria and combinations thereof. Numerical experiments with synthetic and in vivo human data illustrate that the proposed AL algorithms converge faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA.
Statistical image reconstruction for polyenergetic X-ray computed tomography

This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset is used. Applying this method to simulated X-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.
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 for I-131 single photon emission computed tomography (SPECT). 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 updates, 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. Furthermore, it was shown that the TEW correction could be improved for most of the targets investigated here by applying a suitably chosen scaling factor to the scatter estimate. Finally clinical application of MCS was demonstrated by applying the method to an I-131 radioimmunotherapy (RIT) patient study.
Accelerated Nonrigid Intensity-Based Image Registration Using Importance Sampling

Nonrigid image registration methods using intensity-based similarity metrics are becoming increasingly common tools to estimate many types of deformations. Nonrigid warps can be very flexible with a large number of parameters and gradient optimization schemes are widely used to estimate them. However, for large datasets, the computation of the gradient of the similarity metric with respect to these many parameters becomes very time consuming. Using a small random subset of image voxels to approximate the gradient can reduce computation time. This work focuses on the use of importance sampling to reduce the variance of this gradient approximation. The proposed importance sampling framework is based on an edge-dependent adaptive sampling distribution designed for use with intensity-based registration algorithms. We compare the performance of registration based on stochastic approximations with and without importance sampling to that using deterministic gradient descent. Empirical results, on simulated magnetic resonance brain data and real computed tomography inhale-exhale lung data from eight subjects, show that a combination of stochastic approximation methods and importance sampling accelerates the registration process while preserving accuracy.
Modeling the distance-dependent blurring in transmission imaging in the ordered-subset transmission (OSTR) algorithm by using an unmatched projector/backprojector pair

In SPECT, accurate emission reconstruction requires attenuation compensation with high-quality attenuation maps. Resolution loss in transmission maps could cause blurring and artifacts in emission reconstruction. For a transmission system employing parallel-hole collimators and a sheet source, distance-dependent blurring is caused by the non-ideal source and camera collimations, and the finite intrinsic resolution of the detector. These can be approximately modeled by an incremental-blurring model. To compensate for this blurring in iterative transmission reconstruction, we incorporated the incremental blurring model in the forward projector of the OSTR algorithm but did not include the blur in the backprojector. To evaluate our approach, we simulated transmission projections of the MCAT phantom using a ray-tracing projector, in which the rays coming out from a source point form a narrow cone. The geometric blurring due to the non-ideal source and camera collimations was simulated by multiplying the counts along each cone-beam ray with a weight calculated from the overall geometric response function (assumed a two-dimensional Gaussian function), and then summing the weighted counts into projections. The resulting projections were convolved with the intrinsic response (another two-dimensional Gaussian) to simulate the total system blurring of transmission imaging. Poisson noise was then added to the projection data. We also acquired two sets of transmission measurements of an air-filled Data Spectrum Deluxe SPECT phantom on a Prism 2000 scanning-line-source transmission system. We reconstructed the simulations using the OSTR algorithm, with and without modeling of the incremental blur in the projector. The scaling parameter of the penalty prior was optimized in each case by minimizing the root-mean-square error (RMSE). Reconstructions showed that modeling the incremental blur improved the resolution of the attenuation map and quantitative accuracy
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 at each iteration, the CPU time 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 and simulated PET transmission scans.
Baseband detection of bistatic electron spin signals in magnetic resonance force microscopy

In magnetic resonance force microscopy (MRFM), it is hypothesized that it is possible to detect the presence of a single electron spin in a sample volume by measuring spin-induced at-tonewton forces using a micromachined cantilever. In the oscillating cantilever driven adiabatic reversals (OSCAR) method for single-spin MRFM, electron spins are manipulated by an external radio-frequency (RF) magnetic field to produce small periodic deviations in the resonant frequency of the cantilever. These deviations can be detected by frequency demodulation followed by a filtered energy detector. In this paper, we present an alternative to energy detection methods, based on optimal detection theory and Gibbs sampling. Receiver operating characteristic (ROC) curves and power curves from simulations are shown for realistic MRFM operating conditions. Surprisingly, the proposed detector performs almost identically to the filtered energy detector for the range of conditions we studied.
Accelerated noncartesian sense reconstruction using a majorize-minimize algorithm combining variable-splitting

Magnetic resonance imaging (MRI) provides great flexibility in the choice of k-space sampling trajectories. NonCartesian trajectories exhibit several advantages over Cartesian ones but are less amenable to FFT-based manipulation of k-space data. Thus, existing iterative reconstruction methods for nonCartesian trajectories require relatively more computation (interpolation/gridding in addition to FFTs) and can be slow, especially for (undersampled) parallel MRI. In this work, we focus on SENSE-based regularized image reconstruction for nonCartesian trajectories and propose a majorize-minimize approach where we first majorize the SENSE data-fidelity term with a quadratic form involving a symmetric positive definite circulant matrix. For the minimization step, we apply a suitable variable splitting (VS) strategy combined with the augmented Lagrangian framework and alternating minimization that together decouple the circulant matrix from coil sensitivities and the regularizer. The resulting iterative algorithm admits simple update steps, is amenable to FFT-based matrix inversions due in part to the circulant matrix in the majorizer and provides a natural framework for incorporating a two-step procedure for acceleration. Simulations indicate that the proposed algorithm converges faster than some state-of-the-art VSbased iterative image reconstruction methods for the same problem.
Combining Ordered Subsets and Momentum for Accelerated X-Ray CT Image Reconstruction

Statistical X-ray computed tomography (CT) reconstruction can improve image quality from reduced dose scans, but requires very long computation time. Ordered subsets (OS) methods have been widely used for research in X-ray CT statistical image reconstruction (and are used in clinical PET and SPECT reconstruction). In particular, OS methods based on separable quadratic surrogates (OS-SQS) are massively parallelizable and are well suited to modern computing architectures, but the number of iterations required for convergence should be reduced for better practical use. This paper introduces OS-SQS-momentum algorithms that combine Nesterov's momentum techniques with OS-SQS methods, greatly improving convergence speed in early iterations. If the number of subsets is too large, the OS-SQS-momentum methods can be unstable, so we propose diminishing step sizes that stabilize the method while preserving the very fast convergence behavior. Experiments with simulated and real 3D CT scan data illustrate the performance of the proposed algorithms.