nils-herrmann/clean_scirepeval huggingface dataset:
Robust maximum-likelihood position estimation in scintillation cameras

The authors derive a robust ML (maximum-likelihood) position estimator that accounts for the statistical uncertainty in LSF (light-speed function) measurements. The form of the robust estimator diminishes contributions from the tails of the LSF, where the relative measurement errors are the largest. To evaluate the estimator, the authors simulated one dimension of a position-sensitive detector similar to those in the SPRINT II system.<<ETX>>
Fast, iterative, field-corrected image reconstruction for MRI

Magnetic field inhomogeneities cause distortions in the reconstructed images for non-cartesian k-space MRI (using spirals, for example). Several noniterative methods are currently used to compensate for the off-resonance during the reconstruction, but these methods rely on the assumption of a smoothly varying field map. Recently, iterative methods have been proposed that do not rely on this assumption and have the potential to estimate undistorted field maps, but suffer from prohibitively long computation times. In this abstract we present a min-max derived, time-segmented approximation to the signal equation for MRI that, when combined with the nonuniform fast Fourier transform, provides a fast, accurate field-corrected image reconstruction.
Fast Spatial Resolution Analysis of Quadratic Penalized Least-Squares Image Reconstruction With Separate Real and Imaginary Roughness Penalty: Application to fMRI

Penalized least-squares iterative image reconstruction algorithms used for spatial resolution-limited imaging, such as functional magnetic resonance imaging (fMRI), commonly use a quadratic roughness penalty to regularize the reconstructed images. When used for complex-valued images, the conventional roughness penalty regularizes the real and imaginary parts equally. However, these imaging methods sometimes benefit from separate penalties for each part. The spatial smoothness from the roughness penalty on the reconstructed image is dictated by the regularization parameter(s). One method to set the parameter to a desired smoothness level is to evaluate the full width at half maximum of the reconstruction method’s local impulse response. Previous work has shown that when using the conventional quadratic roughness penalty, one can approximate the local impulse response using an FFT-based calculation. However, that acceleration method cannot be applied directly for separate real and imaginary regularization. This paper proposes a fast and stable calculation for this case that also uses FFT-based calculations to approximate the local impulse responses of the real and imaginary parts. This approach is demonstrated with a quadratic image reconstruction of fMRI data that uses separate roughness penalties for the real and imaginary parts.
Hybrid Poisson/polynomial objective functions for tomographic image reconstruction from transmission scans

This paper describes rapidly converging algorithms for computing attenuation maps from Poisson transmission measurements using penalized-likelihood objective functions. We demonstrate that an under-relaxed cyclic coordinate-ascent algorithm converges faster than the convex algorithm of Lange (see ibid., vol.4, no.10, p.1430-1438, 1995), which in turn converges faster than the expectation-maximization (EM) algorithm for transmission tomography. To further reduce computation, one could replace the log-likelihood objective with a quadratic approximation. However, we show with simulations and analysis that the quadratic objective function leads to biased estimates for low-count measurements. Therefore we introduce hybrid Poisson/polynomial objective functions that use the exact Poisson log-likelihood for detector measurements with low counts, but use computationally efficient quadratic or cubic approximations for the high-count detector measurements. We demonstrate that the hybrid objective functions reduce computation time without increasing estimation bias.
Predicting ROC curves for source detection under model mismatch

Performance predictions and test thresholds for the task of detecting a gamma-ray source in background with a position-sensitive detector are often costly to compute empirically. The asymptotic distributions of test statistics for detecting a point-source in background give reasonable performance predictions in terms of the receiver operating characteristic curve (ROC) with less simulated or measured data than empirical methods. The asymptotic distributions also allow the user to determine the proper threshold to achieve a desired false alarm rate. Typically only approximate models are available or practical for complex gamma-ray imaging systems such as 3D position-sensitive semiconductor detectors. Applying standard formulas for the asymptotic distributions of maximum likelihood (ML) estimates in the presence of model mismatch can yield inaccurate and sometimes overly optimistic predictions of detection performance. We apply the theory of the asymptotic distribution of ML estimates under model mismatch to the case of detecting a point-source in background with a 3D position-sensitive CdZn Te detector employing an approximate model for the system response. We show that performance predictions computed using an asymptotic approximation that accounts for mismatch more closely match the empirical performance than predictions generated by an asymptotic approximation that ignores model mismatch.
Compensation for nonuniform resolution using penalized-likelihood reconstruction in space-variant imaging systems

Imaging systems that form estimates using a statistical approach generally yield images with nonuniform resolution properties. That is, the reconstructed images possess resolution properties marked by space-variant and/or anisotropic responses. We have previously developed a space-variant penalty for penalized-likelihood (PL) reconstruction that yields nearly uniform resolution properties . We demonstrated how to calculate this penalty efficiently and apply it to an idealized positron emission tomography (PET) system whose geometric response is space-invariant. In this paper, we demonstrate the efficient calculation and application of this penalty to space-variant systems. (The method is most appropriate when the system matrix has been precalculated.) We apply the penalty to a large field of view PET system where crystal penetration effects make the geometric response space-variant, and to a two-dimensional single photon emission computed tomography system whose detector responses are modeled by a depth-dependent Gaussian with linearly varying full-width at half-maximum. We perform a simulation study comparing reconstructions using our proposed PL approach with other reconstruction methods and demonstrate the relative resolution uniformity, and discuss tradeoffs among estimators that yield nearly uniform resolution. We observe similar noise performance for the PL and post-smoothed maximum-likelihood (ML) approaches with carefully matched resolution, so choosing one estimator over another should be made on other factors like computational complexity and convergence rates of the iterative reconstruction. Additionally, because the postsmoothed ML and the proposed PL approach can outperform one another in terms of resolution uniformity depending on the desired reconstruction resolution, we present and discuss a hybrid approach adopting both a penalty and post-smoothing.
Model-Based Reconstruction of Spectral and Spatial Source Distribution for Objects With Known Motion

Radiation imaging has many applications ranging from health care to homeland security and defense, and source motion is present in many of these applications. When the motion profile of the source is known or otherwise estimated, one can use motion-compensation techniques to reduce blur in the reconstructed image. In this paper, we present a model-based source-intensity reconstruction in the energy and spatial domains using list-mode data. The model includes separate parameterization for objects moving with known motion that is independent of the stationary backdrop. This approach corrects for object motion without smearing stationary sources in the backdrop space. The goal is to simultaneously obtain an estimate of the incident energy and spatial distribution of the radiation field for the stationary backdrop and for each moving object. Experimental Compton-imaging results using an 18-detector array of 3-D-position-sensitive CdZnTe detectors show that the method can successfully reconstruct the source intensity of moving objects while also revealing stationary sources in the backdrop. Also, by modeling the possibility of partial photon energy deposition in the detector, the incident energy spectrum is reconstructed more accurately.
Online dynamic MRI reconstruction via robust subspace tracking

We propose an efficient online reconstruction algorithm for the problem of highly undersampled dynamic magnetic resonance imaging (DMRI). Our approach reconstructs the dynamic time series by processing only a small batch of frames at a time. We adapt an online subspace tracking algorithm based on manifold optimization to the DMRI reconstruction setting and propose a novel extension of the algorithm to enable robust subspace tracking based on a local low-rank plus transform sparse model. Our experiments on real and synthetic data show that proposed approach gives comparable results to methods that reconstruct the entire image series at once while requiring only a fraction of the memory and computational demand. The dramatic memory savings allows robust subspace-based methods to be applied to much larger datasets than previously allowed.
Spatial resolution analysis of iterative image reconstruction with separate regularization of real and imaginary parts

A common method of improving the conditioning in iterative image reconstruction is to include regularization in the reconstruction algorithm. One such regularization is the roughness penalty, which when used in the algorithm encourages smoother images. For complex valued images, the roughness penalty typically penalizes equally the real and imaginary parts. The desired resolution of the reconstructed image can then be evaluated using the local impulse response. A fast algorithm to calculate it was developed for the typical roughness penalty, used for matching the regularization parameter expediently to the desired resolution. For some cases its advantageous to penalize independently the real and imaginary parts. This paper proposes a fast algorithm to calculate the local impulse response for that penalty and applies it to an fMRI reconstruction problem
Three-dimensional non-local edge-preserving regularization for PET transmission reconstruction

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 avoid smoothing of edges, which are important image attributes, various edge-preserving regularization schemes have been proposed. Most of these schemes rely on information from a local neighborhood to determine the presence of edges. In this paper, we propose an objective function that incorporates non-local boundary information into the 3-D regularization method. We use an alternating minimization algorithm with deterministic annealing to minimize the proposed objective function to jointly estimate region boundary surfaces and object pixel values. We apply variational techniques implemented using level sets to update the boundary estimates; then, using the most recent boundary information, we minimize a space-variant quadratic objective function to update the image estimate. We present three-dimensional reconstructions from real PET transmission data.
Discriminative sliding preserving regularization in medical image registration

Sliding effects often occur along tissue/organ boundaries. For instance, it is widely observed that the lung and diaphragm slide against the rib cage and the atria during breathing. Conventional homogeneous smooth registration methods fail to address this issue. Some recent studies preserve motion discontinuities by either using joint registration/segmentation or utilizing robust regularization energy on the motion field. However, allowing all types of discontinuities is not strict enough for physical deformations. In particular, flows that generate local vacuums or mass collisions should be discouraged by the energy functional. In this study, we propose a regularization energy that encodes a discriminative treatment of different types of motion discontinuities. The key idea is motivated by the Helmholtz-Hodge decomposition, and regards the underlying motion flow as a superposition of a solenoidal component, an irrotational component and a harmonic part. The proposed method applies a homogeneous penalty on the divergence, discouraging local volume change caused by the irrotational component, thus avoiding local vacuum or collision; it regularizes the curl field with a robust functional so that the resulting solenoidal component vanishes almost everywhere except on a singular set where the large shear values are preserved. This singularity set corresponds to sliding interfaces. Preliminary tests with both simulated and clinical data showed promising results.
Analysis of observer performance in detecting signals with location uncertainty 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 in the reconstructed images. To reflect medical practice realistically, we consider the location of the lesion to be unknown. This location uncertainty significantly complicates the mathematical analysis of model observer performance. We consider model observers whose decisions are based on finding the maximum value of a local test statistic over all possible locations. Khurd and Gindi (SPIE 2004) and Qi and Huesman (SPIE 2004) described analytical approximations of the moments of the local test statistics and used Monte Carlo simulations to evaluate the localization performance of such "maximum observers". We propose here an alternative approach, where tail probability approximations developed by Adler (AAP 2000) facilitate analytical evaluation of the detection performance of these observers. We illustrate how these approximations can be used to evaluate the probability of detection (for low probability of false alarm operating points) for the maximum channelized hotelling observer. Using our analyses, one can rank and optimize image reconstruction methods without requiring time-consuming Monte Carlo simulations.
Spatial resolution and noise properties of regularized motion-compensated image reconstruction

Reducing motion artifacts is an important problem in medical image reconstruction. Using gating to partition data into separate frames can reduce motion artifacts but can increase noise in images reconstructed fromindividual frames. One can pool the frames to reduce noise by using motion-compensated image reconstruction (MCIR) methods. MCIR methods have been studied in many medical imaging modalities to reduce both noise and motion artifacts. However, there has been less analysis of the spatial resolution and noise properties of MCIR methods. This paper analyzes the spatial resolution and noise properties of MCIR methods based on a general parametric motion model. For simplicity we consider the motion to be given. We present a method to choose quadratic spatial regularization parameters to provide predictable resolution properties that are independent of the object and the motion. The noise analysis shows that the estimator variance depends on both the measurement covariance and the Jacobian determinant values of the motion. A 2D PET simulation demonstrates the theoretical results.
Separate Magnitude and Phase Regularization via Compressed Sensing

Compressed sensing (CS) has been used for accelerating magnetic resonance imaging acquisitions, but its use in applications with rapid spatial phase variations is challenging, e.g., proton resonance frequency shift (PRF-shift) thermometry and velocity mapping. Previously, an iterative MRI reconstruction with separate magnitude and phase regularization was proposed for applications where magnitude and phase maps are both of interest, but it requires fully sampled data and unwrapped phase maps. In this paper, CS is combined into this framework to reconstruct magnitude and phase images accurately from undersampled data. Moreover, new phase regularization terms are proposed to accommodate phase wrapping and to reconstruct images with encoded phase variations, e.g., PRF-shift thermometry and velocity mapping. The proposed method is demonstrated with simulated thermometry data and in vivo velocity mapping data and compared to conventional phase corrected CS.
Regularized MR coil sensitivity estimation using augmented Lagrangian methods

Several magnetic resonance (MR) parallel imaging techniques require explicit estimates of the receive coil sensitivity profiles. These estimates must be accurate over both the object and its surrounding regions to avoid generating artifacts in the reconstructed images. Statistical estimation methods provide robust sensitivity estimates but can be computationally expensive. In this paper, we propose an augmented Lagrangian (AL) based method that estimates the coil sensitivity by minimizing a quadratic cost function. This method reformulates the finite differencing matrix to allow for exact alternating minimization steps. We also explore a variation of our algorithm that involves intermediate updating of the Lagrange multipliers. We demonstrate that our proposed algorithm converges in half the time of the traditional conjugate gradient method with a circulant preconditioner (PCG) on a real data set.
Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms

We present two types of globally convergent relaxed ordered subsets (OS) algorithms for penalized-likelihood image reconstruction in emission tomography: modified block sequential regularized expectation-maximization (BSREM) and relaxed OS separable paraboloidal surrogates (OS-SPS). The global convergence proof of the existing BSREM (De Pierro and Yamagishi, 2001) required a few a posteriori assumptions. By modifying the scaling functions of BSREM, we are able to prove the convergence of the modified BSREM under realistic assumptions. Our modification also makes stepsize selection more convenient. In addition, we introduce relaxation into the OS-SPS algorithm (Erdogan and Fessler, 1999) that otherwise would converge to a limit cycle. We prove the global convergence of diagonally scaled incremental gradient methods of which the relaxed OS-SPS is a special case; main results of the proofs are from (Nedic and Bertsekas, 2001) and (Correa and Lemarechal, 1993). Simulation results showed that both new algorithms achieve global convergence yet retain the fast initial convergence speed of conventional unrelaxed ordered subsets algorithms.
Improved penalized likelihood reconstruction of anatomically correlated emission data

This paper presents a method for incorporating anatomical NMR boundary side information into penalized maximum likelihood (PML) emission image reconstructions. The NMR boundary is parameterized as a periodic spline curve of fixed order and number of knots that is known a priori. Maximum likelihood (ML) estimation of the spline coefficients yields an "extracted" boundary, which is used to define a set of Gibbs weights on the emission image space. These weights, when coupled with a quadratic penalty function, create an edge-preserving penalty that incorporates our prior knowledge effectively. Qualitative analysis demonstrates that our method results in smooth images that do not suffer loss of edge contrast, while quantitative estimates of bias and variance for various values of the smoothing parameter show an improvement over standard quadratically penalized maximum likelihood.
Quantitative I-131 SPECT reconstruction using CT side information from hybrid imaging

A penalized-likelihood (PL) SPECT reconstruction method using a modified regularizer that accounts for anatomical boundary side information was implemented to achieve accurate estimates of both the total target activity and the activity distribution within targets. In both simulations and experimental I-131 phantom studies, reconstructions from 1) penalized likelihood employing CT-side information based regularization (PL-CT); 2) penalized likelihood with edge preserving regularization (no CT); 3) penalized likelihood with conventional spatially invariant quadratic regularization (no CT) were compared with 4) Ordered Subset Expectation Maximization (OSEM), which is the iterative algorithm conventionally used in clinics for quantitative SPECT. Evaluations included phantom studies with perfect and imperfect (misregistered) side information and studies with uniform and non-uniform activity distributions in the target. For targets with uniform activity, the PL-CT images and profiles were closest to the ‘truth’, avoided the edge offshoots evident with OSEM and minimized the blurring across boundaries evident with regularization without CT information. Apart from visual comparison, reconstruction accuracy was evaluated using the bias and standard deviation (STD) of the total target activity estimate and the root mean square error (RMSE) of the activity distribution within the target. PL-CT reconstruction reduced both bias and RMSE compared with regularization without side information. When compared with unregularized OSEM, PL-CT reduced RMSE and STD while bias was comparable. For targets with nonuniform activity, these improvements with PL-CT were observed only when the change in activity was matched by a change in the anatomical image and the corresponding inner boundary was also used to control the regularization. In summary, the present work demonstrates the potential of using CT side information to obtain improved estimates of the activity distribution in targets without sacrificing the accuracy of total target activity estimation.
Conjugate-gradient preconditioning methods for shift-variant PET image reconstruction

Gradient-based iterative methods often converge slowly for tomographic image reconstruction and image restoration problems, but can be accelerated by suitable preconditioners. Diagonal preconditioners offer some improvement in convergence rate, but do not incorporate the structure of the Hessian matrices in imaging problems. Circulant preconditioners can provide remarkable acceleration for inverse problems that are approximately shift-invariant, i.e., for those with approximately block-Toeplitz or block-circulant Hessians. However, in applications with nonuniform noise variance, such as arises from Poisson statistics in emission tomography and in quantum-limited optical imaging, the Hessian of the weighted least-squares objective function is quite shift-variant, and circulant preconditioners perform poorly. Additional shift-variance is caused by edge-preserving regularization methods based on nonquadratic penalty functions. This paper describes new preconditioners that approximate more accurately the Hessian matrices of shift-variant imaging problems. Compared to diagonal or circulant preconditioning, the new preconditioners lead to significantly faster convergence rates for the unconstrained conjugate-gradient (CG) iteration. We also propose a new efficient method for the line-search step required by CG methods. Applications to positron emission tomography (PET) illustrate the method.