nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive structured low rank algorithm for MR image recovery

We introduce an adaptive structured low rank algorithm to recover MR images from their undersampled Fourier coefficients. The image is modeled as a combination of a piece-wise constant component and a piecewise linear component. The Fourier coefficients of each component satisfy an annihilation relation, which results in a structured Toeplitz matrix. We exploit the low rank property of the matrices to formulate a combined regularized optimization problem, which can be solved efficiently. Numerical experiments indicate that the proposed algorithm provides improved recovery performance over the previously proposed algorithms.
On model-based MR spectroscopic imaging

In this paper, we analyze the model-based reconstruction framework in MR spectroscopic imaging and derive an exact expression for the reconstruction error. Based on the insight provided by the analysis, we propose two modifications: (a) we introduce a new echo-planar imaging sequence to acquire more Fourier samples in the same scan duration, since increasing the number of Fourier samples will decrease the bias in the reconstructions. We then use the model-based framework to effectively compensate for the corresponding loss in signal to noise ratio/measurement, (b) we observe that model misfit can affect the reconstructions in constrained imaging much more seriously than standard Fourier reconstruction. We propose a data adaptive basis selection procedure to reduce the misfit, without significantly increasing the noise variance. Both these improvements together enable high-resolution reconstructions that are more robust and with fewer artifacts as compared to reduced encoding, without increasing the scan time
Free breathing cardiac perfusion MRI reconstruction using a sparse and low rank model: Validation with the Physiologically Improved NCAT phantom

We recently proposed an accelerated dynamic magnetic resonance imaging (MRI) reconstruction algorithm that exploits the underlying low rank and sparse properties of the data to achieve highly accelerated reconstructions. In this paper, we validate our algorithm in the context of dynamic free breathing cardiac Perfusion MRI on the Physiologically Improved Non Uniform Cardiac Torso Phantom, PINCAT phantom. The practical utilities of our scheme in providing significantly better reconstructions at higher accelerations in comparison to existing methods are studied. We demonstrate that our scheme do not have trade offs with accurate temporal modeling and spatial quality unlike the existing low rank based schemes. Our results also show the capability of our scheme to achieve better reconstruction qualities at high accelerations in comparison to using only the low rank or sparsity properties individually. We argue that the speed up obtained by our scheme could be capitalized in perfusion imaging to provide better spatio-temporal resolutions and volume coverage while the subject is freely breathing.
Exact computation of area moments for spline and wavelet curves

We present an exact algorithm for the computation of the moments of a region bounded by a curve represented in a scaling function or wavelet basis. Using Green's theorem, we show that the computation of the area moments is equivalent to applying a suitable multidimensional filter on the coefficients of the curve and thereafter computing a scalar product. We compare this algorithm with existing methods such as pixel-based approached and approximation of the region by a polygon.
Recovery of point clouds on surfaces: Application to image reconstruction

We introduce a framework for the recovery of points on a smooth surface in high-dimensional space, with application to dynamic imaging. We assume the surface to be the zero-level set of a bandlimited function. We show that the exponential maps of the points on the surface satisfy annihilation relations, implying that they lie in a finite dimensional subspace. We rely on nuclear norm minimization of the maps to recover the points from noisy and undersampled measurements. Since this direct approach suffers from the curse of dimensionality, we introduce an iterative reweighted algorithm that uses the "kernel trick". The resulting algorithm has similarities to iterative algorithms used in graph signal processing (GSP); this framework can be seen as a continuous domain alternative to discrete GSP theory. The use of the algorithm in recovering free breathing and ungated cardiac data shows the potential of this framework in practical applications.
Shape estimation of 3-D DNA molecules from stereo cryo-electron micro-graphs

We introduce a 3-D parametric active contour algorithm for the shape estimation of DNA molecules from stereo cryo-electron micrographs. We consider a 3-D filament (consisting of a B-spline skeleton and a specified radial profile) and match its projections with the micrographs using an optimization algorithm. To accelerate the evaluation of the projections, we approximate the global model locally by an elongated blob-like template that is designed to be projection-steerable. This means that the 2-D projections of the template at any 3-D orientation can be expressed as a linear combination of a few basis functions. Thus, the matching of the template projections is reduced to evaluating a weighted sum of the inner-products between the basis functions and the micrographs. We choose an internal energy term that penalizes the total curvature magnitude of the curve. We also use a constraint energy term that forces the curve to have a specified length. The sum of these terms along with the image energy obtained from the matching process is minimized using a conjugate-gradient algorithm. We validate the algorithm using real as well as simulated data.
Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing

Achieving simultaneously high angular and spatial resolution in diffusion imaging is challenging because of the long acquisition times involved. We propose a novel compressed sensing method to acquire high angular and spatial resolution diffusion imaging data, while keeping the scan time reasonable. We show that joint under sampling of 6-D k-q space is more efficient than undersampling only one of the dimensions. We use a sparse Gaussian mixture model and an iterative reconstruction scheme to recover the peaks of the orientation distribution functions (ODF) with high accuracy. We show that at least 6-fold acceleration of acquisition is possible, thereby enabling high angular and spatial resolution diffusion imaging in a reasonable scan time.
Two step recovery of jointly sparse and low-rank matrices: Theoretical guarantees

We introduce a two step algorithm with theoretical guarantees to recover a jointly sparse and low-rank matrix from undersampled measurements of its columns. The algorithm first estimates the row subspace of the matrix using a set of common measurements of the columns. In the second step, the subspace aware recovery of the matrix is solved using a simple least square algorithm. The results are verified in the context of recovering CINE data from undersampled measurements; we obtain good recovery when the sampling conditions are satisfied.
Leakage power minimization in deep sub-micron technology by exploiting positive slacks of dependent paths

Leakage power minimization is one of the key aspects of modern multi-million low power system-on-chip (SoC) design. In post timing-closure phase, leakage-in-place-optimization (LIPO) is generally adopted to reduce leakage power by swapping high-leaky cells in the timing-data-paths by low-leaky ones of the same footprint. The traditional LIPO does not touch the clock network for leakage recovery. This paper investigates the opportunity to reduce leakage power further of an already leakage-power-minimized (by LIPO), timing closed design by minimally altering the balanced clock tree. The proposed method, Opportunistic LIPO, intends to borrow unused positive-slack from downstream (and/or upstream) paths, may or may not be at immediate neighborhood, and provide a “positive skew” (and/or “negative skew”) at the capture (and/or launch) clock edge of the current path. In this way, the proposed scheme creates an opportunity in the current path to increase the low-leaky cells distribution. Experimental results, computed over some practical duration (less than 48 hours), on some industry-standard design based on 28nm technology, of having around 50 million gates, shows that the proposed algorithm, “Opportunistic LIPO”, achieves 10-30% better leakage power as compared to traditional LIPO without increasing the number of timing violations and having no significant impact on overall area.
Low-Power Design Strategies for Mobile Computing

The advent of nanometer design process has enabled the integration of multi-million gates with a variety of functionality as a system-on-chip (SoC). The demand for high levels of integration in SoCs are fueled by a strong demand in consumer oriented products for hand held computing, multimedia and other communication products. For these products, power budget is a very critical factor deciding the battery life, size and weight of the portable devices. Designers need to use energy reduction techniques to support as many design features and functions and still keep within the system power budget. The tutorial presents a comprehensive introduction to low power design techniques, and challenges in various facets of the design process. We present an in-depth introduction to concepts with a holistic view to overcome the various challenges and present strategies with a practical approach to the key issues in the design of low power solutions. All the techniques are discussed with practical examples.
Subspace based low rank & joint sparse matrix recovery

We consider the recovery of a low rank and jointly sparse matrix from under sampled measurements of its columns. This problem is highly relevant in the recovery of dynamic MRI data with high spatio-temporal resolution, where each column of the matrix corresponds to a frame in the image time series; the matrix is highly low-rank since the frames are highly correlated. Similarly the non-zero locations of the matrix in appropriate transform/frame domains (e.g. wavelet, gradient) are roughly the same in different frame. The superset of the support can be safely assumed to be jointly sparse. Unlike the classical multiple measurement vector (MMV) setup that measures all the snapshots using the same matrix, we consider each snapshot to be measured using a different measurement matrix. We show that this approach reduces the total number of measurements, especially when the rank of the matrix is much smaller than than its sparsity. Our experiments in the context of dynamic imaging shows that this approach is very useful in realizing free breathing cardiac MRI.
Improved Model-Based Magnetic Resonance Spectroscopic Imaging

Model-based techniques have the potential to reduce the artifacts and improve resolution in magnetic resonance spectroscopic imaging, without sacrificing the signal-to-noise ratio. However, the current approaches have a few drawbacks that limit their performance in practical applications. Specifically, the classical schemes use less flexible image models that lead to model misfit, thus resulting in artifacts. Moreover, the performance of the current approaches is negatively affected by the magnetic field inhomogeneity and spatial mismatch between the anatomical references and spectroscopic imaging data. In this paper, we propose efficient solutions to overcome these problems. We introduce a more flexible image model that represents the signal as a linear combination of compartmental and local basis functions. The former set represents the signal variations within the compartments, while the latter captures the local perturbations resulting from lesions or segmentation errors. Since the combined set is redundant, we obtain the reconstructions using sparsity penalized optimization. To compensate for the artifacts resulting from field inhomogeneity, we estimate the field map using alternate scans and use it in the reconstruction. We model the spatial mismatch as an affine transformation, whose parameters are estimated from the spectroscopy data.
Nonlocal Regularization of Inverse Problems: A Unified Variational Framework

We introduce a unifying energy minimization framework for nonlocal regularization of inverse problems. In contrast to the weighted sum of square differences between image pixels used by current schemes, the proposed functional is an unweighted sum of inter-patch distances. We use robust distance metrics that promote the averaging of similar patches, while discouraging the averaging of dissimilar patches. We show that the first iteration of a majorize-minimize algorithm to minimize the proposed cost function is similar to current nonlocal methods. The reformulation thus provides a theoretical justification for the heuristic approach of iterating nonlocal schemes, which re-estimate the weights from the current image estimate. Thanks to the reformulation, we now understand that the widely reported alias amplification associated with iterative nonlocal methods are caused by the convergence to local minimum of the nonconvex penalty. We introduce an efficient continuation strategy to overcome this problem. The similarity of the proposed criterion to widely used nonquadratic penalties (e.g., total variation and lp semi-norms) opens the door to the adaptation of fast algorithms developed in the context of compressive sensing; we introduce several novel algorithms to solve the proposed nonlocal optimization problem. Thanks to the unifying framework, these fast algorithms are readily applicable for a large class of distance metrics.
Generalized Higher Degree Total Variation (HDTV) Regularization

We introduce a family of novel image regularization penalties called generalized higher degree total variation (HDTV). These penalties further extend our previously introduced HDTV penalties, which generalize the popular total variation (TV) penalty to incorporate higher degree image derivatives. We show that many of the proposed second degree extensions of TV are special cases or are closely approximated by a generalized HDTV penalty. Additionally, we propose a novel fast alternating minimization algorithm for solving image recovery problems with HDTV and generalized HDTV regularization. The new algorithm enjoys a tenfold speed up compared with the iteratively reweighted majorize minimize algorithm proposed in a previous paper. Numerical experiments on 3D magnetic resonance images and 3D microscopy images show that HDTV and generalized HDTV improve the image quality significantly compared with TV.
Design of steerable filters for feature detection using canny-like criteria

We propose a general approach for the design of 2D feature detectors from a class of steerable functions based on the optimization of a Canny-like criterion. In contrast with previous computational designs, our approach is truly 2D and provides filters that have closed-form expressions. It also yields operators that have a better orientation selectivity than the classical gradient or Hessian-based detectors. We illustrate the method with the design of operators for edge and ridge detection. We present some experimental results that demonstrate the performance improvement of these new feature detectors. We propose computationally efficient local optimization algorithms for the estimation of feature orientation. We also introduce the notion of shape-adaptable feature detection and use it for the detection of image corners.
Robust non-local regularization framework for motion compensated dynamic imaging without explicit motion estimation

We introduce an regularized reconstruction scheme to recover dynamic imaging datasets with significant inter frame motion from undersampled Fourier data. The proposed nonlocal regularization penalty is an unweighted sum of distances between image patch pairs in the 3-D dataset. We use robust distance metrics to compute the distance between image patches; these metrics encourage the smoothing between similar patches, while discouraging the averaging of dissimilar patches. Hence, this algorithm is capable of exploiting the similarities between patch pairs in adjacent frames even when they are well separated due to motion, eventhough it does not perform explicit motion estimation. Unlike current non-local regularization schemes, the proposed penalty does not need good initial guesses to estimate the weights. Hence, this approach is readily applicable to accelerated dynamic imaging problems, where good initial guesses are challenging to obtain. The validation of the proposed scheme on numerical phantoms and dynamic MRI datasets demonstrate the superior performance of the proposed scheme over current dynamic imaging schemes.
Improved higher degree total variation (HDTV) regularization

The main focus of this paper is to further improve the performance of the recently introduced higher degree total variation (HDTV) penalties, which are L1-Lp; p ≥ 1 norms of directional image derivatives. We generalize this class as the L1-Lp norms of image responses to rotated versions of an arbitrary derivative operator. We show that several penalties proposed by other researchers are special cases of the generalized isotropic penalties (p = 2), when the derivative operator is chosen appropriately; our experiments show that the anisotropic (p = 1) versions of these penalties provide improved reconstructions. In addition, we optimize the derivative operator for improved orientation selectivity, thus further improving the ability of the resulting penalties to provide high quality image reconstructions. We also focus on the efficient discretization of HDTV penalties, which are specified in the continuous domain. Specifically, we approximate the derivative operators as the sum of partial derivatives of an almost isotropic B-spline window. Our numerical experiments confirm the benefit of the improved discretization and the optimization of the operator.
Reduction of distortions in MRSI using a new signal model

We propose a new reconstruction scheme for magnetic resonance spectroscopic imaging (MRSI) signal based on minimizing the spatial total variation (TV) integrated with the l1 -norm of the spectral data. Furthermore, we propose to represent the MRSI signal as a linear combination of polynomials and spikes to capture the baseline and metabolite peaks. The proposed signal model provides a sparser representation of the spectral data, which enables us to suppress the field inhomogeneity induced line shape distortions and losses as well as reduce noise. We also take advantage of high resolution field map and anatomical prior derived from additional 3-D MRI scan to model system imperfections.
Robust Reconstruction of MRSI Data Using a Sparse Spectral Model and High Resolution MRI Priors

We introduce a novel algorithm to address the challenges in magnetic resonance (MR) spectroscopic imaging. In contrast to classical sequential data processing schemes, the proposed method combines the reconstruction and postprocessing steps into a unified algorithm. This integrated approach enables us to inject a range of prior information into the data processing scheme, thus constraining the reconstructions. We use high resolution, 3-D estimate of the magnetic field inhomogeneity map to generate an accurate forward model, while a high resolution estimate of the fat/water boundary is used to minimize spectral leakage artifacts. We parameterize the spectrum at each voxel as a sparse linear combination of spikes and polynomials to capture the metabolite and baseline components, respectively. The constrained model makes the problem better conditioned in regions with significant field inhomogeneity, thus enabling the recovery even in regions with high field map variations. To exploit the high resolution MR information, we formulate the problem as an anatomically constrained total variation optimization scheme on a grid with the same spacing as the magnetic resonance imaging data. We analyze the performance of the proposed scheme using phantom and human subjects. Quantitative and qualitative comparisons indicate a significant improvement in spectral quality and lower leakage artifacts.
3-D shape estimation of DNA molecules from stereo cryo-electron micro-graphs using a projection-steerable snake

We introduce a three-dimensional (3-D) parametric active contour algorithm for the shape estimation of DNA molecules from stereo cryo-electron micrographs. We estimate the shape by matching the projections of a 3-D global shape model with the micrographs; we choose the global model as a 3-D filament with a B-spline skeleton and a specified radial profile. The active contour algorithm iteratively updates the B-spline coefficients, which requires us to evaluate the projections and match them with the micrographs at every iteration. Since the evaluation of the projections of the global model is computationally expensive, we propose a fast algorithm based on locally approximating it by elongated blob-like templates. We introduce the concept of projection-steerability and derive a projection-steerable elongated template. Since the two-dimensional projections of such a blob at any 3-D orientation can be expressed as a linear combination of a few basis functions, matching the projections of such a 3-D template involves evaluating a weighted sum of inner products between the basis functions and the micrographs. The weights are simple functions of the 3-D orientation and the inner-products are evaluated efficiently by separable filtering. We choose an internal energy term that penalizes the average curvature magnitude. Since the exact length of the DNA molecule is known a priori, we introduce a constraint energy term that forces the curve to have this specified length. The sum of these energies along with the image energy derived from the matching process is minimized using the conjugate gradients algorithm. We validate the algorithm using real, as well as simulated, data and show that it performs well.
Denoising and deinterleaving of EPSI data using structured low-rank matrix recovery

Echo-planar spectroscopic imaging (EPSI) sequence with spectrally interleaving is often used to rapidly collect metabolic MRI data. The main problem in using it on high field scanners is the presence of spurious peaks resulting from phase distortions between interleaves as well as the low signal to noise ratio. We introduce a novel structured low-rank framework for the simultaneous denoising and deinterleaving of spectrally interleaved EPSI data. The proposed algorithm exploits annihilation relations resulting from the linear predicability of exponential signals as well as due to uncorrected phase relations between interleaves. The algorithm is formulated as a structured nuclear norm minimization of a block Hankel matrix, derived from the interleaves. Experiments using hyperpolarized 13C mouse kidney EPSI data demonstrate the ability of the algorithm to remove ghost peaks from EPSI data collected using bipolar readout gradients.