nils-herrmann/clean_scirepeval huggingface dataset:
Sparse regularization in MRI iterative reconstruction using GPUs

Regularization is a common technique used to improve image quality in inverse problems such as MR image reconstruction. In this work, we extend our previous Graphics Processing Unit (GPU) implementation of MR image reconstruction with compensation for susceptibility-induced field inhomogeneity effects by incorporating an additional quadratic regularization term. Regularization techniques commonly impose the prior information that MR images are relatively smooth by penalizing large changes in intensity between neighboring voxels. However, the associated computations often increase data access and the overall computational load, which can lead to slower image reconstruction. This motivates us to adopt a GPU-enabled implementation of spatial regularization using sparse matrices. This implementation enables the computations for the entire reconstruction procedure to be done on the GPU, which avoids the memory bandwidth bottlenecks associated with frequent communications between the GPU and CPU. Both the CPU and GPU code of this implementation will be available for release at the time of the conference.
Low-Rank Modeling of Local  $k$-Space Neighborhoods (LORAKS) for Constrained MRI

Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-rank matrices when the image has limited spatial support or slowly varying phase. Based on this, we develop a novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS). A new regularization penalty and corresponding algorithm for promoting low-rank are also introduced. The potential of LORAKS is demonstrated with simulated and experimental data for a range of denoising and sparse-sampling applications. LORAKS is also compared against state-of-the-art methods like homodyne reconstruction, l1-norm minimization, and total variation minimization, and is demonstrated to have distinct features and advantages. In addition, while calibration-based support and phase constraints are commonly used in existing methods, the LORAKS framework enables calibrationless use of these constraints.
REMOVAL OF LIPID SIGNAL IN MRSI USING SPATIAL-SPECTRAL CONSTRAINTS

Analysis and quantification of magnetic resonance spectroscopic imaging (MRSI) data is complicated by the presence of lipid nuisance signals in the spectra. These signals typically appear as peaks with amplitudes much larger than those of the metabolites of interest and, in the case of lipids, present broad, distorted lineshapes. Although the problem of lipid signal removal by postprocessing has been addressed in the past both in the context of single voxel spectroscopy (SVS) and MRSI, existing approaches use either spatial or spectral constraints to determine the lipid component in the signal. This paper introduces a method that incorporates both types of constraints for improved removal of lipid signals. Specifically, this method uses an anatomical image of the lipid locations to spatially constrain the lipid estimate as well as a field inhomogeneity map to improve spectral fitting of the lipid lineshape. Experimental results are shown to demonstrate the performance of the proposed method
Causality in variance in electrophysiological data using the ARCH model

Measurements of electrophysiological activity can be used to infer interactions between different regions of the human brain. In this work, we consider the use of an autoregressive conditional heteroscedasticity (ARCH) model to estimate causality in variance between different brain regions in simulation and continuously measured EEG data. We propose an efficient new algorithm for ARCH model estimation and demonstrate that the proposed approach provides promising results that are distinct from the causality estimates obtained from simpler and more conventional signal causality models.
New linear transforms for data on a Fourier 2-sphere with application to diffusion MRI

This paper describes a new family of linear transforms for data restricted to the surface of a 2-sphere in three-dimensional Fourier space. These transforms generalize the existing Funk-Radon Transform, which has previously been used with great success to extract microstructural tissue orientation information from high angular resolution magnetic resonance diffusion imaging data. Several properties of the new transforms are described, and computationally efficient implementations are derived using spherical harmonic basis functions. A special case from this family, called the FunkRadon and Cosine Transform, is introduced and evaluated. The method is illustrated with simulated and real diffusion weighted MRI data.
Low rank matrix recovery for real-time cardiac MRI

Real-time cardiac MRI is a very challenging problem because of limitations on imaging speed and resolution. To address this problem, the (k,t) - space MR signal is modeled as being partially separable along the spatial and temporal dimensions, which results in a rank-deficient data matrix. Image reconstruction is then formulated as a low-rank matrix recovery problem, which is solved using emerging low-rank matrix recovery techniques. In this paper, the Power Factorization algorithm is applied to efficiently recover the cardiac data matrix. Promising results are presented to demonstrate the performance of this novel approach.
A network flow method for improved MR field map estimation in the presence of water and fat

Field map estimation is an important problem in MRI, with applications such as water/fat separation and correction of fast acquisitions. However, it constitutes a nonlinear and severely ill-posed problem requiring regularization. In this paper, we introduce an improved method for regularized field map estimation, based on a statistically motivated formulation, as well as a novel algorithm for the solution of the corresponding optimization problem using a network flow approach. The proposed method provides theoretical guarantees (local optimality with respect to a large move), as well as an efficient implementation. It has been applied to the water/fat separation problem and tested on a number of challenging datasets, showing high-quality results.
Accelerating MR image reconstruction on GPUs

With the explosive development of advanced image reconstruction algorithms, there is an urgent need for acceleration of these algorithms to facilitate their use in practical applications. This paper describes our experience using graphics processing units (GPUs) for advanced MR image reconstruction from non-Cartesian data. We show that implementation of MR image reconstruction on NVIDIA CUDA-enabled GPUs can significantly accelerate the solution of this type of image reconstruction problem. Given the acceleration afforded by the GPU, we expect our strategy to be applied to other computationally intensive imaging algorithms.
MS-FRACT: Optimized linear transform methods for ODF estimation in multi-shell diffusion MRI

This paper proposes a new linear transform approach for multi-shell ODF estimation that has been theoretically characterized and does not require modeling assumptions. The proposed approach, called MS-FRACT, is optimized over the class of all rotationally-invariant linear transforms to yield accurate ODFs. Due to linearity, MS-FRACT yields stable and predictable results, and can be directly interpreted with respect to the true diffusion propagator. The performance of MS-FRACT is illustrated using simulated and real data. In addition to proposing MS-FRACT, a new theoretical framework is also described that can be applied to arbitrary multi-shell linear ODF estimation methods.
High-resolution cardiac MRI using partially separable functions and weighted spatial smoothness regularization

Imaging of cardiac morphology and functions in high spatiotemporal resolution using MRI is a challenging problem due to limited imaging speed and the inherent tradeoff between spatial resolution, temporal resolution, and signal-to-noise ratio (SNR). The partially separable function (PSF) model has been shown to achieve high spatiotemporal resolution but can lead to noisy reconstructions. This paper proposes a method to improve the SNR and reduce artifacts in PSF-based reconstructions through the use of anatomical constraints. These anatomical constraints are obtained from a high-SNR image of composite (k, t)-space data (summed along the time axis) and used to regularize the PSF reconstruction. The method has been evaluated on experimental data of rat hearts to achieve 390 εm in-plane resolution and 15 ms temporal resolution.
High-Resolution Diffusion MRI

This paper presents a magnetic resonance imaging method that can provide high-resolution images characterizing water diffusion in biological tissues. These images contain information about tissue microstructure, thereby providing a useful means to monitor physiological changes. The proposed method overcomes the long-standing problem of limited signal- to-noise ratio with diffusion MRI by using penalized maximum-likelihood reconstruction. Experiments performed on a mouse brain illustrate the ability of the technique to elucidate high resolution structural detail that would not be visible using other non-invasive approaches.
The Fourier radial error spectrum plot: A more nuanced quantitative evaluation of image reconstruction quality

In the modern biomedical image reconstruction literature, the quality of a reconstructed image is often numerically quantified using scalar error measures such as mean-squared error or the structural similarity index. While such measures provide a rough summary of image quality, they also suffer from well-known limitations. For example, a substantial amount of information is necessarily lost whenever the characteristics of a high-dimensional image are summarized by a single number. In this work, we introduce the Fourier radial Error Spectrum Plot (ESP), which provides a novel and more nuanced assessment of error by decomposing the error into its different spatial frequency components. The usefulness of ESP is illustrated in the context of MRI reconstruction from under-sampled data. In addition, we demonstrate that the extra dimension of insight provided by ESP can be used to improve the performance of existing image reconstruction techniques.
Canonical Granger causality applied to functional brain data

Dynamic images of functional activity in the brain offer the potential to measure connectivity between regions of interest. We want to measure causal activity between regions of interest (ROIs) with signals recorded from multiple channels or voxels in each ROI. Previous methods, such as Granger causality, look for causality between individual time series; hence, they suffer from local interactions or interferers obscuring signals of interest between two ROIs. We propose a metric that reduces the effect of interference by taking weighted sums of sensors in each ROI, as is done with canonical correlation. Hence, we measure region-to-region, rather than channel-to-channel or point-to-point, Granger causality. We show in simulation that our “canonical Granger causality” accurately mimics the underlying structure with few samples, unlike current methods of multivariate Granger causality. We then use anatomically relevant regions of interest in a visuomotor task in a multichannel intracortical EEG study to infer the direction of transmission in visual processing.
Low-rank approximations for dynamic imaging

This paper describes a framework for dynamic imaging based on the representation of a spatiotemporal image as a low-rank matrix. This kind of image modeling is flexible enough to accurately and parsimoniously represent a wide range of dynamic imaging data. Representation using a low-rank model leads to new schemes for data acquisition and image reconstruction, enabling reconstruction from highly-undersampled datasets. Theoretical considerations and algorithms are discussed, and empirical results are provided to illustrate the performance of the approach.
Probing in vivo microstructure with T1-T2 relaxation correlation spectroscopic imaging

Quantitative MR relaxometry can provide unique subvoxel information about the microscopic tissue compartments that are present in a large imaging voxel. However, unambiguously distinguishing between these tissue compartments continues to be challenging with conventional methods due to the illposedness of the inverse problem. This paper describes a new imaging approach, which we call T1 Relaxation-T2 Relaxation Correlation Spectroscopic Imaging (RR-CSI), that uses two-dimensional relaxation encoding combined with spatially-constrained reconstruction to help overcome illposedness. Results are shown with real data, including what we believe to be the first in vivo demonstration of multidimensional relaxation correlation spectroscopic imaging.
Autocalibrated loraks for fast constrained MRI reconstruction

Low-rank modeling of local k-space neighborhoods (LO-RAKS) is a recent novel framework for reconstructing MRI images from sparsely-sampled and/or noisy data. Previously-proposed LORAKS-based reconstruction approaches relied on low-rank matrix recovery methods, which were powerful but computationally expensive. In this work, we demonstrate that substantial computational accelerations can be achieved if the nullspaces associated with the low-rank LORAKS matrices are pre-estimated from autocalibration data. In addition to improving computation speed, we also show that auto-calibrated LORAKS can have substantial advantages over previous autocalibrated parallel imaging methods.
A quadratic majorize-minimize framework for statistical estimation with noisy rician- and noncentral chi-distributed MR images

The statistics of noisy MR magnitude and square-root sum-of-squares MR images are well-described by the Rice and noncentral chi distributions, respectively. Statistical estimation involving these distributions is complicated by the facts that they have first- and second-order moments that depend nonlinearly on the noiseless image, and can have nonconvex negative log-likelihoods. This paper proposes a new majorize-minimize framework to ease the computational burden associated with statistical estimation involving these distributions. We derive quadratic tangent majorants for the negative log-likelihoods, which enables statistical cost functions to be optimized using a sequence of much simpler least-squares or regularized least-squares surrogate problems. We demonstrate the use of this framework in the context of regularized MR image denoising, with both simulated and experimental data.
Robust autocalibrated loraks for EPI ghost correction

Nyquist ghosts are a longstanding problem in a variety of fast MRI experiments that use echo-planar imaging (EPI). Recently, several structured low-rank matrix modeling approaches have been proposed that achieve state-of-the-art ghost-elimination, although the performance of these approaches is still inadequate in several important scenarios. We present a new structured low-rank matrix recovery ghost correction method that we call Robust Autocalibrated LORAKS (RAC-LORAKS). RAC-LORAKS incorporates constraints from autocalibration data to avoid ill-posedness, but allows adaptation of these constraints to gain robustness against possible autocalibration imperfections. RAC-LORAKS is tested in two challenging scenarios: highly-undersampled multi-channel EPI of the brain, and cardiac EPI with a doubleoblique slice orientation. Results show that RAC-LORAKS can provide substantial improvements over existing ghost correction methods, and potentially enables new imaging applications that were previously confounded by ghost artifacts.
Recognition of Degraded Handwritten Characters Using Local Features

The main problems of Optical Character Recognition (OCR) systems are solved if printed latin text is considered. Since OCR systems are based upon binary images, their results are poor if the text is degraded. In this paper a codex consisting of ancient manuscripts is investigated. Due to environmental effects the characters of the analyzed codex are washed out which leads to poor results gained by state of the art binarization methods. Hence, a segmentation free approach based on local descriptors is being developed. Regarding local information allows for recognizing characters that are only partially visible. In order to recognize a character the local descriptors are initially classified with a Support Vector Machine (SVM) and then identified by a voting scheme of neighboring local descriptors. State of the art local descriptor systems are evaluated in this paper in order to compare their performance for the recognition of degraded characters.
Skew Estimation of Sparsely Inscribed Document Fragments

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document for further processing. A pre-processing step of document analysis methods is a skew estimation of scanned or photographed documents. Current skew estimation methods require the existence of large text areas, are dependent on the text type and can be limited on a specific angle range. The proposed method is gradient based in combination with a Focused Nearest Neighbor Clustering of interest points and has no limitations regarding the detectable angle range. The upside/down decision is based on statistical analysis of ascenders and descenders. It can be applied to entire documents as well as to document fragments containing only a few words. Results show that the proposed skew estimation is comparable with state-of-the-art methods and outperforms them on a real dataset consisting of 658 snippets.
A Gradient Extension of Center Symmetric Local Binary Patterns for Robust RGB-NIR Image Matching

Scene acquisition using RGB and Near Infra-Red (NIR) filters generates useful visual information about scene contents. But it induces significant intensity and textural changes between RGB and NIR images of the same scene. It becomes a challenging problem to perform interest point based image matching under such intensity and textural changes. To cope with this problem, a novel method for the description of interest points is proposed. The method proposed is based on Center Symmetric-Local Binary Patterns (CS-LBP) which extracts distinct image features from intensity and gradient magnitude maps of the image patches centered at interest points. Those features are then used in the SIFT algorithm to compute robust descriptors against intensity and textural changes. The experimental results show that the method proposed improves the descriptor matching between RGB and NIR images and achieves better image matching results than CS-LBP and SIFT based methods for the description of interest points.
Combining Spectral and Spatial Features for Robust Foreground-Background Separation

Foreground-background separation in multispectral images of damaged manuscripts can benefit from both, spectral and spatial information. Therefore, we incorporate a Markov Random Field which provides a powerful tool to combine both features simultaneously. Higher order models enable the inclusion of spatial constraints based on stroke characteristics. We apply belief propagation for inference and include the higher order potentials by upgrading the message update. The proposed segmentation method requires no training and is independent of script, size, and style of characters. We will demonstrate the robust performance on a set of degraded documents and on synthetic images.
Hierarchical classification of paintings using face- and brush stroke models

It is often difficult to attribute works of art to a certain artist. In the case of paintings, radiological methods like X-ray and infra-red diagnosis, digital radiography, computer-tomography, etc. and color analyzes are employed to authenticate works of art. But all these methods do not relate certain characteristics of an art work to a specific artist-the artist's personal style. In order to study this personal style, we examine the "structural signature" based on brush strokes in particular in portrait miniatures. A computer-aided classification and recognition system for portrait miniatures is developed, which enables a semi-automatic classification based on brush strokes. A hierarchically structured classification scheme is introduced which separates the classification into three different levels of information: color, shape of region, and structure of brush strokes.
Proposing features for the reconstruction of marble plates of ephesos

The reconstruction of marble plates of Ephesos is discussed in this paper. Automated reconstruction techniques exist for 2 dimensional objects as well as for 3 dimensional objects. Current applications that consider the shape of the fragments only, are able to reconstruct complete objects consisting of no more than e.g. 10 parts. Therefore, additional features are necessary to reconstruct objects with a large number of fragments, or if pieces of different objects are mixed up. This paper presents an overview of currently used 2D features for reconstruction on the example of paper reconstruction, how 3D objects like pottery can be reconstructed and shows how these 2D features can be used for the reassembling of the Ephesos marble plates.
Torn Document Analysis as a Prerequisite for Reconstruction

An automated assembling of torn documents (2D) will support philologists, archaeologists and forensic experts. Especially if the amount of fragments is large (up to 1000), a human puzzle solver will not be feasible due to cost and time. Ancient manuscripts may be broken due to bad storage conditions, or documents are manually torn to make the information unreadable. In Germany a project to reconstruct the torn "Stasi-files" is running for historical investigations. Also disasters like the collapse of the historical archive of the city of cologne (Germany), where a large part of the archived manuscripts have been destroyed, need algorithms to reconstruct torn manuscripts and books. The automated solving can be divided into shape based matching techniques (apictorial) or techniques that analyze the visual content of the fragments (pictorial) too. Artifacts like broken and lost pieces or overlapping parts of fragments increase the error rate of shape based matching techniques. Therefore a combined approach of document analysis and shape matching is necessary for large instances of this problem. In this paper the preliminary snippet processing is described where the orientation of fragments, as well as the content like paper color and the color of the inks used is analyzed. The methods presented, are evaluated on database consisting of 690 snippets of Stasi files which were manually annotated to provide groundtruth data.
Lifelogging keyframe selection using image quality measurements and physiological excitement features

Keyframe selection is the process of finding a representative frame in an image sequence. Although mostly known from video processing, keyframe selection faces new challenges in the lifelog domain. To obtain a keyframe that is close to a user-selected frame, we propose a keyframe selection method based on image quality measurements and excitement features. Image quality measurements such as contrast, color variance, sharpness, noise and saliency are used to filter high quality images. However, high quality images are not necessarily keyframes because humans also use emotions in the selection process. In this study, we employ a biosensor to measure the excitement of humans. In previous investigation, keyframe selection using only image quality measurements yielded an acceptance rate of 79.70%. Our proposed method achieves an acceptance rate of 84.45%.