nils-herrmann/clean_scirepeval huggingface dataset:
Compressed-Sensing MRI With Random Encoding

Compressed sensing (CS) has the potential to reduce magnetic resonance (MR) data acquisition time. In order for CS-based imaging schemes to be effective, the signal of interest should be sparse or compressible in a known representation, and the measurement scheme should have good mathematical properties with respect to this representation. While MR images are often compressible, the second requirement is often only weakly satisfied with respect to commonly used Fourier encoding schemes. This paper investigates the use of random encoding for CS-MRI, in an effort to emulate the “universal” encoding schemes suggested by the theoretical CS literature. This random encoding is achieved experimentally with tailored spatially-selective radio-frequency (RF) pulses. Both simulation and experimental studies were conducted to investigate the imaging properties of this new scheme with respect to Fourier schemes. Results indicate that random encoding has the potential to outperform conventional encoding in certain scenarios. However, our study also indicates that random encoding fails to satisfy theoretical sufficient conditions for stable and accurate CS reconstruction in many scenarios of interest. Therefore, there is still no general theoretical performance guarantee for CS-MRI, with or without random encoding, and CS-based methods should be developed and validated carefully in the context of specific applications.
Motion compensation for reference-constrained image reconstruction from limited data

Using a reference image (or a template) to constrain image reconstruction from limited data is becoming more and more popular in various imaging applications. However, in order for a reference/template to be a useful constraint, it has to be correctly aligned with the target image to be determined. This paper addresses this new image registration problem of registering a high-resolution image to a target image of which only limited measurements are available. We solve this problem using an intermediate image model, which expresses the target image as a combination of a generalized series (with basis constructed from a motion-dependent reference image) and a residual component. An algorithm is proposed to determine the motion parameters. Performance of the proposed method has been analyzed by computer simulations. Accurate motion compensation is demonstrated. The proposed method is expected to make image reconstruction using prior information from a reference more robust in the presence of object motion.
On MR experiment design with quadratic regularization

The design of MRI experiments represents a trade-off between acquisition time, signal-to-noise ratio (SNR), and resolution. For fixed acquisition time and reconstruction resolution, it has been widely believed that the optimal acquisition strategy is to avoid collecting k-space data at frequencies higher than the nominal image resolution. While this belief is true under certain metrics, we observe in this work that a high-resolution acquisition strategy, combined with an appropriate linear filtering/regularization strategy, leads to significantly improved SNR/resolution efficiency for the majority of common resolution metrics. Analysis of this surprising result leads to practical methods for the improved design of imaging experiments and the selection of efficient quadratic regularization penalties.
Image Reconstruction From Highly Undersampled $( {\bf k}, {t})$-Space Data With Joint Partial Separability and Sparsity Constraints

Partial separability (PS) and sparsity have been previously used to enable reconstruction of dynamic images from undersampled ( k,t)-space data. This paper presents a new method to use PS and sparsity constraints jointly for enhanced performance in this context. The proposed method combines the complementary advantages of PS and sparsity constraints using a unified formulation, achieving significantly better reconstruction performance than using either of these constraints individually. A globally convergent computational algorithm is described to efficiently solve the underlying optimization problem. Reconstruction results from simulated and in vivo cardiac MRI data are also shown to illustrate the performance of the proposed method.
Improved efficiency for microstructure imaging using high-dimensional MR correlation spectroscopic imaging

Magnetic Resonance Correlation Spectroscopic Imaging (MR-CSI) is a high-dimensional imaging approach that our group recently introduced for studying microstructure. MR-CSI uses imaging data that is simultaneously encoded with multiple MR contrast mechanisms, and applies a spatial-spectral reconstruction framework to estimate a spectroscopic image with a high-dimensional correlation spectrum for every spatial location. While MR-CSI enables powerful new capabilities for spatially mapping sub-voxel microstructural compartments, the contrast encoding can be slow due to the curse of dimensionality. This paper reviews MR-CSI and investigates a principled estimation theoretic approach that can enable more efficient experiment designs.
Correction of field inhomogeneity effects on limited k-space MRSI data using anatomical constraints

Magnetic field inhomogeneity is a long-standing problem in magnetic resonance imaging (MRI) and spectroscopic imaging (MRSI). Specifically, in MRSI, field inhomogeneity, if not corrected, can cause frequency shifts, line broadening, and lineshape distortions in the spectral peaks. This paper addresses the problem of correcting the field inhomogeneity effects on limited k-space MRSI data. A penalized maximum-likelihood method is proposed, which enables the use of anatomical constraints for improving the correction performance with only limited k-space data. Simulation results are shown to demonstrate the effectiveness of the proposed method.
SMS-LORAKS: Calibrationless simultaneous multislice MRI using low-rank matrix modeling

Conventional approaches to accelerated simultaneous mul-tislice (SMS) MRI rely on structured k-space sampling and parallel imaging with known coil sensitivity profiles. In this paper, we introduce a novel framework for SMS MRI that is flexible enough to accommodate a number of different experimental variations: it supports both single-channel and parallel imaging data, both calibration-based and calibration-less k-space sampling trajectories, and Hadamard, Fourier, and random-phase non-Fourier encoding along the slice dimension. Our proposed SMS framework is based on the recently introduced LORAKS framework (low-rank matrix modeling of local k-space neighborhoods). The new framework, which we call SMS-LORAKS, is evaluated using real retrospectively undersampled k-space data. These evaluations confirm the promise and flexibility of the proposed approach.
Accelerating iterative field-compensated MR image reconstruction on GPUs

We propose a fast implementation for iterative MR image reconstruction using Graphics Processing Units (GPU). In MRI, iterative reconstruction with conjugate gradient algorithms allows for accurate modeling the physics of the imaging system. Specifically, methods have been reported to compensate for the magnetic field inhomogeneity induced by the susceptibility differences near the air/tissue interface in human brain (such as orbitofrontal cortex). Our group has previously presented an algorithm for field inhomogeneity compensation using magnetic field map and its gradients. However, classical iterative reconstruction algorithms are computationally costly, and thus significantly increase the computation time. To remedy this problem, one can utilize the fact that these iterative MR image reconstruction algorithms are highly parallelizable. Therefore, parallel computational hardware, such as GPU, can dramatically improve their performance. In this work, we present an implementation of our field inhomogeneity compensation technique using NVIDA CUDA(Compute Unified Device Architecture)-enabled GPU. We show that the proposed implementation significantly reduces the computation times around two orders of magnitude (compared with non-GPU implementation) while accurately compensating for field inhomogeneity.
Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints

Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying optimization problem efficiently. The proposed method has been validated using simulated cardiac imaging data, with the expected capability to reduce image artifacts and reconstruction noise.
Rank-Constrained Solutions to Linear Matrix Equations Using PowerFactorization

Algorithms to construct/recover low-rank matrices satisfying a set of linear equality constraints have important applications in many signal processing contexts. Recently, theoretical guarantees for minimum-rank matrix recovery have been proven for nuclear norm minimization (NNM), which can be solved using standard convex optimization approaches. While nuclear norm minimization is effective, it can be computationally demanding. In this work, we explore the use of the powerfactorization (PF) algorithm as a tool for rank-constrained matrix recovery. Empirical results indicate that incremented-rank PF is significantly more successful than NNM at recovering low-rank matrices, in addition to being faster.
Deconvolved Spatial Light Interference Microscopy for Live Cell Imaging

Spatial light interference microscopy (SLIM) is a recently developed method for the label-free imaging of live cells, using the quantitative optical path length through the sample as an endogenous source of contrast. In conventional SLIM, spatial resolution is limited by diffraction and aberrations. This paper describes a novel constrained deconvolution method for improving resolution in SLIM. Constrained deconvolution is enabled by experimental measurement of the system point-spread function and the modeling of coherent image formation in SLIM. Results using simulated and experimental data demonstrate that the proposed method leads to significant improvements in the resolution and contrast of SLIM images. The proposed method should prove useful for high-resolution label-free studies of biological cells and subcellular processes.
PSF model-based reconstruction with sparsity constraint: Algorithm and application to real-time cardiac MRI

The partially separable function (PSF) model has been successfully used to reconstruct cardiac MR images with high spatiotemporal resolution from sparsely sampled (k,t)-space data. However, the underlying model fitting problem is often ill-conditioned due to temporal undersampling, and image artifacts can result if reconstruction is based solely on the data consistency constraints. This paper proposes a new method to regularize the inverse problem using sparsity constraints. The method enables both partial separability (or low-rankness) and sparsity constraints to be used simultaneously for high-quality image reconstruction from undersampled (k,t)-space data. The proposed method is described and reconstruction results with cardiac imaging data are presented to illustrate its performance.
The equivalence of linear spherical deconvolution and model-free linear transform methods for diffusion MRI

This work provides a theoretical analysis of linear spherical deconvolution methods in diffusion MRI, building off of a theoretical framework that was previously developed for model-free linear transforms of the Fourier 2-sphere. It is demonstrated that linear spherical deconvolution methods have an equivalent representation as model-free linear transform methods. This perspective is used to study the characteristics of linear spherical deconvolution from the point of view of the diffusion propagator. Practical results are shown with experimental brain MRI data.
Constrained mixture modeling for the estimation of kinetic parameters in dynamic PET

The estimation and analysis of kinetic parameters in dynamic PET is frequently confounded by noise and partial volume effects. We propose a new constrained model of dynamic PET to address these limitations. The proposed formulation incorporates an explicit partial volume model in which each image voxel is represented as a mixture of different pure tissue types with distinct temporal dynamics. A two stage algorithm is proposed to solve the resulting problem. In the first stage, a sparse signal processing method is applied to estimate the rate parameters for the different tissue compartments from the noisy PET time series. In the second stage, tissue fractions and the linear parameters of different time activity curves (TACs) are estimated using a combination of sparsity, spatial-regularity, and fractional mixture constraints. A block coordinate descent (BCD) algorithm is combined with a manifold search to robustly estimate these parameters. The method is evaluated with both simulated and experimental dynamic PET data.
Optimal experiment design for magnetic resonance fingerprinting.

Magnetic resonance (MR) fingerprinting is an emerging quantitative MR imaging technique that simultaneously acquires multiple tissue parameters in an efficient experiment. In this work, we present an estimation-theoretic framework to evaluate and design MR fingerprinting experiments. More specifically, we derive the Cramér-Rao bound (CRB), a lower bound on the covariance of any unbiased estimator, to characterize parameter estimation for MR fingerprinting. We then formulate an optimal experiment design problem based on the CRB to choose a set of acquisition parameters (e.g., flip angles and/or repetition times) that maximizes the signal-to-noise ratio efficiency of the resulting experiment. The utility of the proposed approach is validated by numerical studies. Representative results demonstrate that the optimized experiments allow for substantial reduction in the length of an MR fingerprinting acquisition, and substantial improvement in parameter estimation performance.
Correcting inhomogeneity-induced distortion in FMRI using non-rigid registration

Magnetic field inhomogeneities in echo planar images (EPI) can cause large distortion in the phase encoding dimension. In functional MRI (fMRI), this distortion can shift activation loci, increase inter subject variability, and reduce statistical power during group analysis. Distortion correction methods that make use of acquired magnetic field maps have been developed, however, field maps are not always acquired or may not be available to researchers. An alternative approach, which we pursue in this paper, is to estimate the distortion retrospectively by spatially registering the EPI to a structural MRI. We describe a constrained non-linear registration method for correcting fMRI distortion that uses T1-weighted images and does not require field maps. We compared resting state results from uncorrected fMRI, fMRI data corrected with field maps, and fMRI data corrected with our proposed method in data from 20 subjects. The results show that the estimated field maps were similar to the acquired field maps and that the proposed method reduces the overall error in independent component location.
Nonnegative matrix factorization for tissue mixture modeling with noisy MR magnitude image sequences

Nonnegative matrix factorization (NMF) is a powerful blind source separation method that can be used for nonpara-metric partial volume mixture modeling in a variety of high-dimensional medical imaging experiments. However, conventional NMF methods can fail to produce meaningful results when the measurements contain substantial non-Gaussian noise. This paper proposes a new NMF modeling approach that is appropriate for noisy MRI magnitude images that follow the noncentral chi (NCC) statistical distribution. We formulate a maximum likelihood optimization problem, which we solve by combining conventional least-squares NMF algorithms with a recent majorize-minimize framework for the NCC distribution. This new approach is applied to real diffusion MRI data, and is demonstrated to yield improved results relative to conventional NMF.
A Majorize-Minimize Framework for Rician and Non-Central Chi MR Images

The statistics of many MR magnitude images are described by the non-central chi (NCC) family of probability distributions, which includes the Rician distribution as a special case. These distributions have complicated negative log-likelihoods that are nontrivial to optimize. This paper describes a novel majorize-minimize framework for NCC data that allows penalized maximum likelihood estimates to be obtained by solving a series of much simpler regularized least-squares surrogate problems. The proposed framework is general and can be useful in a range of applications. We illustrate the potential advantages of the framework with real and simulated data in two contexts: 1) MR image denoising and 2) diffusion profile estimation in high angular resolution diffusion MRI. The proposed approach is shown to yield improved results compared to methods that model the noise statistics inaccurately and faster computation relative to commonly-used nonlinear optimization techniques.
High-Resolution MR Metabolic Imaging

Magnetic resonance spectroscopic imaging has been recognized for a long time as a powerful tool for biochemical imaging. However, its practical utility is still rather limited due to poor spatial resolution, low signal-to-noise ratio, and long data acquisition times. In this work, we propose a new technique that enables reconstruction of metabolite maps with high spatial resolution. This technique uses a statistical model to incorporate known anatomical boundaries for edge-preserving noise filtering. This statistical reconstruction scheme makes it possible to use very noisy data, thereby enabling the collection of high-resolution data in a reasonable amount of time. We illustrate the performance of this method with images of the N-acetyl-L-aspartate distribution from an in vivo mouse brain.
Label-free high-resolution imaging of live cells with deconvolved spatial light interference microscopy

Spatial light interference microscopy (SLIM) is a powerful new quantitative phase optical imaging technique that can be used for studying live cells without the need for exogenous contrast agents. This paper proposes a novel deconvolution-based approach to reconstructing SLIM data, which dramatically improves the visual quality of the images. The proposed deconvolution formulation is tailored to the physics of SLIM imaging of biological samples, and a new fast algorithm is designed for computationally-efficient image reconstruction in this setting. Simulation and experimental results demonstrate that deconvolution can reduce the width of the point-spread function by at least 20%, and can significantly improve the contrast of high-resolution features. Temporally-resolved SLIM imaging with the high spatial resolution enabled by deconvolution provides new opportunities for studying the dynamics of cellular and sub-cellular processes.
Towards optimal linear estimation of orientation distribution functions with arbitrarily sampled diffusion MRI data

The estimation of orientation distribution functions (ODFs) from diffusion MRI data is an important step in diffusion tractography, but existing estimation methods often depend on signal modeling assumptions that are violated by real data, lack theoretical characterization, and/or are only applicable to a small range of q-space sampling patterns. As a result, existing ODF estimation methods may be suboptimal. In this work, we propose a novel ODF estimation approach that learns a linear ODF estimator from training data. The training set contains ideal data samples paired with corresponding ideal ODFs, and the learning procedure reduces to a simple linear least-squares problem. This approach can accommodate arbitrary q-space sampling schemes, can be characterized theoretically, and is theoretically demonstrated to generalize far beyond the training set. The proposed approach is evaluated with simulated and in vivo diffusion data, where it is demonstrated to outperform common alternatives.
Computational imaging with loraks: Reconstructing linearly predictable signals using low-rank matrix regularization

Although many modern computational imaging approaches use sparse or low-rank modeling constraints, it should not be forgotten that many classical computational imaging approaches rely on a different kind of constraint: linear predictability. Several years ago, we introduced the LORAKS framework, which combines classical linear prediction models with modern low-rank matrix completion approaches. LORAKS makes the observation that linear prediction assumptions can be embedded into high-dimensional structured low-rank matrix constraints. This enables a powerful and flexible new approach for modeling and reconstructing images. In this work, we review LORAKS and describe our recent progress.
Spatiotemporal imaging with partially separable functions: A matrix recovery approach

There has been significant recent interest in fast imaging with sparse sampling. Conventional imaging methods are based on Shannon-Nyquist sampling theory. As such, the number of required samples often increases exponentially with the dimensionality of the image, which limits achievable resolution in high-dimensional scenarios. The partially-separable function (PSF) model has previously been proposed to enable sparse data sampling in this context. Existing methods to leverage PSF structure utilize tailored data sampling strategies, which enable a specialized two-step reconstruction procedure. This work formulates the PSF reconstruction problem using the matrix-recovery framework. The explicit matrix formulation provides new opportunities for data acquisition and image reconstruction with rank constraints. Theoretical results from the emerging field of low-rank matrix recovery (which generalizes theory from sparse-vector recovery) and our empirical results illustrate the potential of this new approach.
Super-resolution reconstruction of MR image sequences with contrast modeling

Quantitative MR imaging experiments (e.g., to measure relaxation and diffusion properties of tissues) often require image sequences with different contrast in each frame. However, high-resolution acquisition of each frame can lead to prohibitively long experiments. In this work, we investigate the possibility of utilizing a parametric contrast model to synthesize high-resolution information. Theoretical analysis and empirical evidence indicates that this kind of super-resolution can be possible, though robustness is dependent on a number of factors (e.g., the contrast model and the experiment design). In particular, it is found that conventional low-frequency sampling leads to significant information loss, but that alternative experiments can overcome this limitation. Experimental results are shown in the context of T2* relaxation mapping.
SHAPING SPATIAL RESPONSE FUNCTIONS FOR OPTIMAL ESTIMATION OF COMPARTMENTAL SIGNALS FROM LIMITED FOURIER DATA

The goal of quantitative MRI experiments is often to generate summary statistics regarding the behavior of the image within given regions of interest. In this work, we show that it is possible to generate better estimates for compartmental signals without requiring an initial image reconstruction step. The proposed approach is optimal in that it minimizes the worst case mean-squared error for the class of linear estimators and for a set of signals satisfying known magnitude constraints. In addition, it conveniently results in a criterion by which different experimental parameters can be compared. The extension of this region of interest quantification to image reconstruction is straightforward, as image reconstruction can be thought of as a graphical tiling of region of interest estimates of the signal.