nils-herrmann/clean_scirepeval huggingface dataset:
Translational-invariant dictionaries for compressed sensing in magnetic resonance imaging

A sparse representation is an essential part of compressed sensing (CS). The discrete wavelet transform has been widely used to sparsely represent magnetic resonance images for CS applications. Artifacts usually exist in CS reconstruction when the wavelet transform is used alone. In this work, we investigate improving the image reconstruction quality through redundant translational-invariant sparsifying transforms. Cycle spinning is used with the wavelet transform and overlapping patches are used with the discrete cosine transform to achieve translational invariance. Experimental results show significant improvement in artifact reduction when contrasted with non-translational invariant transforms.
Linear transformations and Restricted Isometry Property

The Restricted Isometry Property (RIP) introduced by Candés and Tao is a fundamental property in compressed sensing theory. It says that if a sampling matrix satisfies the RIP of certain order proportional to the sparsity of the signal, then the original signal can be reconstructed even if the sampling matrix provides a sample vector which is much smaller in size than the original signal. This short note addresses the problem of how a linear transformation will affect the RIP. This problem arises from the consideration of extending the sensing matrix and the use of compressed sensing in different bases. As an application, the result is applied to the redundant dictionary setting in compressed sensing.
Three-dimensional hybrid-encoded MRI using compressed sensing

Compressed sensing (CS) has been successfully applied to accelerate conventional magnetic resonance imaging (MRI) with Fourier encoding. However in Fourier encoding, the low spatial frequency always has to be fully sampled such that the high frequency is insufficiently sampled at high accelerations, leading to serious loss of resolution. Non-Fourier encoding has been studied in the context of compressed sensing for 2-D imaging, but with limited improvement. In this paper, we propose a novel 3-D acquisition method using hybrid encoding. The method exploits random encoding with a circulant structure along the “phase encoding” direction, while keeping conventional Fourier encoding along the readout and slice encoding directions. A 2-D random undersampling pattern is used for accelerating the scans. Both simulation and experimental results demonstrate that the proposed method preserves much better resolution than the conventional 3-D Fourier encoding when the same acceleration factors are used.
An efficient augmented Lagrangian algorithm for graph regularized sparse coding in clustering

The combination of sparse coding and manifold learning has received much attention recently. However, the computational complexity of the resulting optimization problem hinders its practical application. In this paper, an augmented Lagrangian method is proposed to address this issue, which first transforms the unconstrained problem to an equivalent constrained problem and then an alternating direction method is used to iteratively solve the subproblems. Experimental results validate the effectiveness of the propose algorithm.
Joint Estimation of Coil Sensitivities and Image in Parallel Magnetic Resonance Imaging

Parallel magnetic resonance imaging using multi-channel receiver coils has emerged as an effective tool to reduce imaging time in various applications. In general, exact knowledge of the coil sensitivities is needed to reconstruct the desired image. However, the issue of accurate estimation of coil sensitivities has not been fully addressed; thereby the errors limit the image quality achievable with the technology. In this paper, we model the image reconstruction problem as a blind multi-channel image restoration problem such that both the coil sensitivities and the desired images are estimated jointly. Specifically, a parametric model is assumed for the coil sensitivities whose parameters are solved by an iterative optimization algorithm along with image reconstruction. The proposed method has been tested on various data sets. The results from some in vivo data sets are shown to demonstrate the effectiveness of the proposed method especially when a rather large net acceleration factor is used.
A variable projection approach to parallel magnetic resonance imaging

Accurate estimation of channel sensitivity functions is still a challenging problem in parallel imaging. JSENSE has recently been proposed to improve the accuracy of sensitivity estimation using the self calibration data. It regards both the coil sensitivities and the desired images as unknowns to be solved for jointly. The existing algorithm for the underlying nonlinear optimization problem requires an accurate initial value, which needs considerable number of self calibration data. In this paper, we use the variable projection method to find the optimal solution. The method is known to be able to give an optimal solution, and our implementation has a linear convergence rate. The performance of the proposed method is evaluated using a set of in vivo experiment data.
K-T ISD: Compressed sensing with iterative support detection for dynamic MRI

In this paper, we propose a new k−t Iterative Support Detection (k−t ISD) method to improve the CS reconstruction for dynamic cardiac MRI by incorporating additional information on the support of the dynamic image in x−f space. The proposed method uses an iterative procedure for alternating image reconstruction and support detection in x−f space. Experimental results demonstrate that the proposed k−t ISD method improves the reconstruction quality of dynamic cardiac MRI over the basic CS method in which support information is not exploited.
Cross-sampled GRAPPA for parallel MRI

As one widely-used parallel-imaging method, Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) technique reconstructs the missing k-space data by a linear combination of the acquired data using a set of weights. These weights are usually derived from auto-calibration signal (ACS) lines that are acquired in parallel to the reduced lines. In this paper, a cross sampling method is proposed to acquire the ACS lines orthogonal to the reduced lines. This cross sampling method increases the amount of calibration data along the direction that the k-space is undersampled and thus improves the calibration accuracy, especially when a small number of ACS lines are acquired. Both phantom and in vivo experiments demonstrate that the proposed method, named cross-sampled GRAPPA (CS-GRAPPA), can effectively reduce the aliasing artifacts of GRAPPA when high acceleration is desired.
A kernel approach to parallel MRI reconstruction

GRAPPA has been widely used as a k-space-based parallel MRI reconstruction technique. It linearly combines the acquired k-space signals to estimate the missing k-space signals where the coefficients are obtained by linear regression using auto-calibration signals. At high acceleration factors, GRAPPA reconstruction can suffer from a high level of noise even with a large number of auto-calibration signals. In this work, we improve the GRAPPA model using a kernel approach. Specifically, the acquired k-space data are mapped through a nonlinear transform to a high-dimensional space and then linearly combined to estimate the missing k-space data. A polynomial kernel is investigated in this work. Experimental results using phantom and in vivo data demonstrate that the proposed kernel GRAPPA method can significantly improve the reconstruction quality over the existing methods.
Toeplitz random encoding MR imaging using compressed sensing

Compressed Sensing (CS), as a new framework for data acquisition and signal recovery, has been applied to accelerate conventional magnetic resonance imaging (MRI) with Fourier encoding. However, Fourier encoding is not universal and weakly spreads out the energy of most natural images. This limits the achievable reduction factors. In this paper, we propose a Toeplitz random encoding method that is universal and spreads out the image energy more evenly. The MR physical feasibility of the proposed encoding method is verified by Bloch simulation, and the superior performance of the proposed method is demonstrated in simulation results.
Sparsity-based PET image reconstruction using MRI learned dictionaries

Incorporating anatomical information obtained by magnetic resonance (MR) imaging has shown its promises to improve the positron emission tomography (PET) imaging quality. In this paper, we propose a novel maximum a posteriori (MAP) PET image reconstruction technique using a sparse prior whose dictionary is learned from the corresponding MR images. Specifically, a PET image is divided into three-dimensional overlapping patches which are expected to be sparsely represented over a redundant dictionary. With the assumption that the PET and MR images of a patient can be sparsified under a common dictionary, the dictionary is learned from the MR image to involve anatomical measurement in PET image reconstruction. The PET image and its sparse representation are updated alternately in the iterative reconstruction process. We evaluated the performance of the proposed method quantitatively, using a realistic simulation with the BrainWeb database phantoms. Noticeable improvement on the noise versus bias tradeoff has been demonstrated in images reconstructed from the proposed method, compared to that from the conventional smoothness MAP method.
Parallel Mri Reconstruction: A Filter-Bank Approach

Parallel magnetic resonance imaging (MRI) using multi-channel receiver coils has emerged as an effective tool to reduce imaging time in various MRI applications. However, there are still a number of image reconstruction issues that have not been fully addressed, thereby limited the level of speed enhancement achievable with the technology. This paper reformulates the image reconstruction problem as a digital filter bank problem (in contrast to the conventional matrix formulation used in the popular SENSE technique) and develops an effective algorithm to handle the ill-conditioned problem often encountered in image reconstruction. Simulations based on an eight-channel configuration demonstrate that the proposed method is superior to the SENSE reconstruction especially when large acceleration factors are used
SENSE reconstruction with inaccurate sensitivity functions: effects and remedies

Image reconstruction in parallel imaging using an array of phased receiver coils involves solving a matrix equation whose coefficient matrix is formed from estimated coil sensitivity functions. Inaccurate sensitivity functions can create image artifacts and reduce signal-to-noise ratio (SNR). This paper discusses the effects of inaccurate sensitivity functions and proposes some remedies to reduce these effects. Specifically, a spatially varying wavelet denoising method is proposed to improve the accuracy of the estimated sensitivity maps, and a constrained reconstruction method is used to incorporate a priori information about the location of the regions of interest to further improve the SENSE reconstruction.
Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery

This paper establishes a modeling framework for data located onto or close to (unknown) smooth manifolds, embedded in Euclidean spaces, and considers its application to dynamic magnetic resonance imaging (dMRI). The framework comprises several modules: First, a set of landmark points is identified to describe concisely a data cloud formed by highly under-sampled dMRI data, and second, low-dimensional renditions of the landmark points are computed. Searching for the linear operator that decompresses low-dimensional data to high-dimensional ones, and for those combinations of landmark points which approximate the manifold data by affine patches, leads to a bi-linear model of the dMRI data, cognizant of the intrinsic data geometry. Preliminary numerical tests on synthetically generated dMRI phantoms, and comparisons with state-of-the-art reconstruction techniques, underline the rich potential of the proposed method for the recovery of highly under-sampled dMRI data.
Accelerating dynamic magnetic resonance imaging by nonlinear sparse coding

Although being high-dimensional, dynamic magnetic resonance images usually lie on low-dimensional manifolds. Nonlinear models have been shown to capture well that latent low-dimensional nature of data, and can thus lead to improvements in the quality of constrained recovery algorithms. This paper advocates a novel reconstruction algorithm for dynamic magnetic resonance imaging (dMRI) based on nonlinear dictionary learned from low-spatial but high-temporal resolution images. The nonlinear dictionary is initially learned using kernel dictionary learning, and the proposed algorithm subsequently alternates between sparsity enforcement in the feature space and the data-consistency constraint in the original input space. Extensive numerical tests demonstrate that the proposed scheme is superior to popular methods that use linear dictionaries learned from the same set of training data.
Parallel imaging via sparse representation over a learned dictionary

This paper proposes an adaptive reconstruction method for parallel imaging (PI) via sparse representation over a learned dictionary and also a corresponding dictionary learning based PI (DL-PI) algorithm. DL-PI adopts the “divide and conquer” strategy to solve the ℓ2-DL reconstruction formulation, with dictionary learning to capture the structure information and a Taylor approximation to update the target image analytically. The proposed approach has been applied to parallel magnetic resonance imaging (MRI) and compared to two latest state-of-the-art methods. The experimental results on in-vivo data show that the DL-PI algorithm possesses strong ability in detail preservation and is competent in artifact removal during the MR image reconstruction process.
Pseudo 2D random sampling for compressed sensing MRI

The paper presents a novel approach of pseudo 2D random sampling scheme for application of compressed sensing in Cartesian magnetic resonance imaging (MRI). The proposed scheme is realized by a pulse sequence program which switches the directions of phase encoding and frequency encoding during data acquisition such that both kx and ky directions can be undersampled randomly. The resulting random sampling pattern approximates the ideal but impractical 2D patterns. Both the simulation and experiment results show the proposed method is superior to the existing 1D random sampling and similar to the ideal 2D random sampling in terms of the reconstruction quality. This method can potentially improve the MR imaging speed through the application of compressed sensing in conventional MRI.
SENSE reconstruction with nonlocal TV regularization

Ill-conditioning is serious problem in SENSE reconstruction, especially when large acceleration factors are employed. For Cartesian SENSE, Tikhonov regularization and total variation have been commonly used. However, the Tikhonov regularized image usually tends to blur edges and total variation regularization has a blocky effect. In this paper, we propose a new SENSE regularization technique that is based on nonlocal total variation with Bregman iteration. It penalizes highly oscillatory noise and allows sharp edges and fine textures in reconstruction. The method is shown to be able to significantly reduce the artifacts in SENSE reconstruction.
A kernel approach to compressed sensing parallel MRI

Compressed sensing (CS) and parallel imaging (PI) have been widely studied for accelerating MRI reconstruction. Furthermore, the serial combination methods of CS and PI have been proposed for even higher speed of reconstruction. However, both reconstructed signals by CS and PI are not as accurate as acquired MR signals, so that errors of CS reconstruction as the first step will be propagated and even amplified to deteriorate PI reconstruction quality in the second step. Based on our previous work - nonlinear GRAPPA (NLGRAPPA), we proposed a novel serial combination of CS and NLGRAPPA (CS-NLGRAPPA) reconstruction. The generalized kernel regression model of NLGRAPPA can remove error effects of inaccurate signal reconstruction by CS. Experimental results using phantom and in vivo data demonstrate that the proposed CS-NLGRAPPA method can significantly improve the reconstruction quality over the existing method and push net reduction factor around 4.
Efficient GRAPPA reconstruction using random projection

As a data-driven technique, GRAPPA has been widely used for parallel MRI reconstruction. In GRAPPA, a large amount of calibration data is desirable for accurate calibration and thus estimation. However, the computational time increases with the large number of equations to be solved, which is especially serious in 3-D reconstruction. To address this issue, a number of approaches have been developed to compress the large number of physical channels to fewer virtual channels. In this paper, we tackle the complexity problem from a different prospective. We propose to use random projections to reduce the dimension of the problem in the calibration step. Experimental results show that randomly projecting the data onto a lower-dimensional subspace yields results comparable to those of traditional GRAPPA, but is computationally significantly less expensive.
Improved myocardial perfusion PET imaging with MRI learned dictionaries

The purpose of this study is to form PET image reconstruction sparse priors based on MR image learned dictionaries in Bayesian PET image reconstruction and to evaluate the performance in myocardial perfusion (MP) defect detection. A set of time activity curves representing the typical patient Rb-82 bio-distribution was applied in the analytical simulation with 2.5-min and 4.5-min cumulated activities. For each count levels, we used the 4D XCAT phantom to simulate two MP imaging datasets, one with normal MP and the other with a reduced activity region on the left ventricle. Using the SIMRI simulator, MR images were simulated with sequence specified to be 3D T1-weighted as in a clinical PET/MRI protocol. The maximum a posterior (MAP) PET image reconstruction that took dictionary-based sparse approximation of PET images as the prior was applied. Assuming that the PET and MR images can be sparsified under the same dictionary, the K-SVD algorithm was used in the dictionary learning (DL) process from the MR images. The receiver operating characteristic (ROC) analysis on the reconstructed images for perfusion defect detection was performed using a channelized Hotelling observer (CHO). The DL MAP algorithm demonstrated improved noise versus bias tradeoff compared to that from the ML algorithm and also provided better performance in the MP defect detection task.
M-MRI: A manifold-based framework to highly accelerated dynamic magnetic resonance imaging

High-dimensional signals, including dynamic magnetic resonance (dMR) images, often lie on low dimensional manifold. While many current dynamic magnetic resonance imaging (dMRI) reconstruction methods rely on priors which promote low-rank and sparsity, this paper proposes a novel manifold-based framework, we term M-MRI, for dMRI reconstruction from highly undersampled k-space data. Images in dMRI are modeled as points on or close to a smooth manifold, and the underlying manifold geometry is learned through training data, called “navigator” signals. Moreover, low-dimensional embeddings which preserve the learned manifold geometry and effect concise data representations are computed. Capitalizing on the learned manifold geometry, two regularization loss functions are proposed to reconstruct dMR images from highly undersampled k-space data. The advocated framework is validated using extensive numerical tests on phantom and in-vivo data sets.
REGULARIZED SENSE RECONSTRUCTION USING ITERATIVELY REFINED TOTAL VARIATION METHOD

SENSE has been widely accepted as one of the standard reconstruction algorithms for parallel MRI. When large acceleration factors are employed, the SENSE reconstruction becomes very ill-conditioned. For Cartesian SENSE, Tikhonov regularization has been commonly used. However, the Tikhonov regularized image usually tends to be overly smooth, and a high-quality regularization image is desirable to alleviate this problem but is not available. In this paper, we propose a new SENSE regularization technique that is based on total variation with iterated refinement using Bregman iteration. It penalizes highly oscillatory noise but allows sharp edges in reconstruction without the need for prior information. In addition, the Bregman iteration refines the image details iteratively. The method is shown to be able to significantly reduce the artifacts in SENSE reconstruction
Accurate T2 mapping with sparsity and linear predictability filtering

T2 mapping provides a quantitative manner to access tissue structure, composition, water content and iron levels. Nevertheless, due to the relative long scanning time, its practical usage is limited. This paper addresses this problem using a novel iterative nonlinear filtering method to achieve sparse sampling reconstruction. Specifically two filters are involved. One is the soft thresholding operator promoting spatial sparsity and temporal redundancy. The other is the Hankel matrix low-rank approximation enforcing the exponential structure along echo time dimension. The proposed method has been validated based on a brain T2 experiment data, and is shown to provide high image quality.
Artificial Neural Network Enhanced Bayesian PET Image Reconstruction

In positron emission tomography (PET) image reconstruction, the Bayesian framework with various regularization terms has been implemented to constrain the radio tracer distribution. Varying the regularizing weight of a maximum a posteriori (MAP) algorithm specifies a lower bound of the tradeoff between variance and spatial resolution measured from the reconstructed images. The purpose of this paper is to build a patch-based image enhancement scheme to reduce the size of the unachievable region below the bound and thus to quantitatively improve the Bayesian PET imaging. We cast the proposed enhancement as a regression problem which models a highly nonlinear and spatial-varying mapping between the reconstructed image patches and an enhanced image patch. An artificial neural network model named multilayer perceptron (MLP) with backpropagation was used to solve this regression problem through learning from examples. Using the BrainWeb phantoms, we simulated brain PET data at different count levels of different subjects with and without lesions. The MLP was trained using the image patches reconstructed with a MAP algorithm of different regularization parameters for one normal subject at a certain count level. To evaluate the performance of the trained MLP, reconstructed images from other simulations and two patient brain PET imaging data sets were processed. In every testing cases, we demonstrate that the MLP enhancement technique improves the noise and bias tradeoff compared with the MAP reconstruction using different regularizing weights thus decreasing the size of the unachievable region defined by the MAP algorithm in the variance/resolution plane.
Dynamic MRI using spatiotemporal modeling with phased array coils

This paper presents a model-based dynamic MRI method with time-sequential sampling in k-t space using phased array coils, The temporal variation of the dynamic object is modeled as a superposition of motion elements while the spatial variation is captured by the series coefficients, thereby converting the dynamic imaging problem to a parameter identification problem. Parallel data acquisition using phased array coils is used to balance k-space and time-domain undersampling. Simulations based on experimental cardiac data are carried out, which show that the proposed method can reconstruct motion artifacts-free images with very high spatiotemporal resolution