nils-herrmann/clean_scirepeval huggingface dataset:
Identification of myocardial infarction using three-dimensional strain tensor fractional anisotropy

Accurate localization of myocardial viability is important in diagnosis of infarction. Regional strain function provides excessive information for clinical decision making but comparison of strain tensor profiles across differing tissue types is usually difficult due to multi-variate nature of tensors. It is desirable to describe tensors with simplified scalar indices which are more mathematically and statistically intuitive. In this work, anisotropy of tensors in healthy and experimental infarct regions in a large animal model is assessed and compared to directional components of strain tensors which are currently the most popular indices in active use. Myocardial strain tensors are computed using zHARP, a magnetic resonance (MR) tagging technique that provides quantification of cardiac function with direct computation of three-dimensional tensors from two-dimensional short axis MR images. Fractional anisotropy of strain tensors shows high correlation with late gadolinium enhanced images and is capable of discrimination between healthy and infarcted regions.
Medical Imaging: Signals and Systems (Prince, J.L. and Links, J.M.; 2006) [Book Review]

This well-written book on medical imaging is organized into five parts and, in total, consists of 13 chapters emphasizing key overall conceptual divisions. Part 1 ( "Basic Imaging Principles") provides much of the background material needed to follow the rest of the book. Part 2 covers radiographic imaging in three chapters, Part 3 presents nuclear medicine in three chapters; Part 4 addresses ultrasound imaging in two chapters, and Part 5 discusses magnetic resonance imaging in the last two chapters. At the end of each chapter, a "summary and key concepts" sections reviews the main ideas and lists important references. The text also provides numerous examples and homework exercise problems as well as online instructor resources. Overall, the four key features of the book - a good balance of content, a wealth of images and diagrams for presenting key concepts of medical imaging, numerous motivational and biologically relevant examples, and rich online instructor resources - make the nook a very attractive and useful textbook for an upper-level undergraduate course or a graduate-level course in biomedical imaging. Since the book approaches the biomedical imaging topics from a signal processing perspective, it is probably most appropriate for engineering students.
Magnetic Resonance Image Example-Based Contrast Synthesis

The performance of image analysis algorithms applied to magnetic resonance images is strongly influenced by the pulse sequences used to acquire the images. Algorithms are typically optimized for a targeted tissue contrast obtained from a particular implementation of a pulse sequence on a specific scanner. There are many practical situations, including multi-institution trials, rapid emergency scans, and scientific use of historical data, where the images are not acquired according to an optimal protocol or the desired tissue contrast is entirely missing. This paper introduces an image restoration technique that recovers images with both the desired tissue contrast and a normalized intensity profile. This is done using patches in the acquired images and an atlas containing patches of the acquired and desired tissue contrasts. The method is an example-based approach relying on sparse reconstruction from image patches. Its performance in demonstrated using several examples, including image intensity normalization, missing tissue contrast recovery, automatic segmentation, and multimodal registration. These examples demonstrate potential practical uses and also illustrate limitations of our approach.
Optimal tag pattern validation using magnetic resonance imaging

Magnetic resonance (MR) tagging is a technique used to impose a known intensity pattern, called a tag pattern, on an object by spatially modulating the object's proton spin density before the image is acquired. Since the tag pattern moves with the object, MR tagging is particularly useful in the estimation of deformable motion from MR image sequences because some tag patterns can resolve motion ambiguities such as the aperture problem and improve the accuracy of optical flow motion estimates. We present an experimental validation of the tag pattern optimization method of Denney and Prince (see Proc. of the IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, p.V225-V228, Minneapolis, April 1993, and  March 1994) in a practical MR imaging environment. This experiment includes a study of the effects of temporal sampling on the optimal frequency that was not done by Denney et. al. Our experimental results show that the tag pattern optimization method of Denney et. al. is valid in a practical MR imaging application.<<ETX>>
Topology-preserving Geometric Deformable Model on Adaptive Quadtree Grid

Topology-preserving geometric deformable models (TGDMs) are used to segment objects that have a known topology. Their accuracy is inherently limited, however, by the resolution of the underlying computational grid. Although this can be overcome by using fine-resolution grids, both the computational cost and the size of the resulting contour increase dramatically. In order to maintain computational efficiency and to keep the contour size manageable, we have developed a new framework, termed QTGDMs, for topology-preserving geometric deformable models on balanced quadtree grids (BQGs). In order to do this, definitions and concepts from digital topology on regular grids were extended to BQGs so that characterization of simple points could be made. Other issues critical to the implementation of geometric deformable models are also addressed and a strategy for adapting a BQG during contour evolution is presented. We demonstrate the performance of the QTGDM method using both mathematical phantoms and real medical images.
Magnetic Resonance Connectome Automated Pipeline: An Overview

This article presents a novel, tightly integrated pipeline for estimating a connectome. The pipeline utilizes magnetic resonance (MR) imaging (MRI) data to produce a high-level estimate of the structural connectivity in the human brain. The MR connectome automated pipeline (MRCAP) is efficient, and its modular construction allows researchers to modify algorithms to meet their specific requirements. The pipeline has been validated, and more than 200 connectomes have been processed and analyzed to date.
Deformable registration of macular oct using a-mode scan similarity

Optical coherence tomography (OCT) of the macular cube has become an increasingly important tool for investigating and managing retinal pathology. One important new area of investigation is the analysis of anatomic variably across a population. Such an analysis on the retina requires the construction of a normalized space, which is generally created through deformable registration of each subject into a common template. Unfortunately, state-of-the-art 3D registration tools fail to adequately spatially normalize retinal OCT images. This work proposes a new deformable registration algorithm for OCT images using the similarity between pairs of A-mode scans. First, a retinal OCT specific affine step is presented, which uses automated landmarks to perform global translations and individual rescaling of all the subject's Amode scans. Then, a deformable registration using regularized one-dimensional radial basis functions is applied to further align the retinal layers. Results on 15 subjects show the improved accuracy of this approach in comparison to state of the art methods with respect to registration for labeling. Additional results show the ability to generate stereotaxic spaces for retinal OCT.
A Topology Preserving Level Set Method for Geometric Deformable Models

Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models implemented using level set methods have advantages over parametric models due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models-the ability to automatically handle topology changes-turns out to be a liability in applications where the object to be segmented has a known topology that must be preserved. We present a new class of geometric deformable models designed using a novel topology-preserving level set method, which achieves topology preservation by applying the simple point concept from digital topology. These new models maintain the other advantages of standard geometric deformable models including subpixel accuracy and production of nonintersecting curves or surfaces. Moreover, since the topology-preserving constraint is enforced efficiently through local computations, the resulting algorithm incurs only nominal computational overhead over standard geometric deformable models. Several experiments on simulated and real data are provided to demonstrate the performance of this new deformable model algorithm.
Semi-automatic segmentation of the tongue for 3D motion analysis with dynamic MRI

Accurate segmentation is an important preprocessing step for measuring the internal deformation of the tongue during speech and swallowing using 3D dynamic MRI. In an MRI stack, manual segmentation of every 2D slice and time frame is time-consuming due to the large number of volumes captured over the entire task cycle. In this paper, we propose a semi-automatic segmentation workflow for processing 3D dynamic MRI of the tongue. The steps comprise seeding a few slices, seed propagation by deformable registration, random walker segmentation of the temporal stack of images and 3D super-resolution volumes. This method was validated on the tongue of two subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semiautomatic segmentations of 52 volumes showed an average dice similarity coefficient (DSC) score of 0.9 with reduced segmented volume variability compared to manual segmentations.
Reconstruction of High-Resolution Tongue Volumes From MRI

Magnetic resonance images of the tongue have been used in both clinical studies and scientific research to reveal tongue structure. In order to extract different features of the tongue and its relation to the vocal tract, it is beneficial to acquire three orthogonal image volumes-e.g., axial, sagittal, and coronal volumes. In order to maintain both low noise and high visual detail and minimize the blurred effect due to involuntary motion artifacts, each set of images is acquired with an in-plane resolution that is much better than the through-plane resolution. As a result, any one dataset, by itself, is not ideal for automatic volumetric analyses such as segmentation, registration, and atlas building or even for visualization when oblique slices are required. This paper presents a method of superresolution volume reconstruction of the tongue that generates an isotropic image volume using the three orthogonal image volumes. The method uses preprocessing steps that include registration and intensity matching and a data combination approach with the edge-preserving property carried out by Markov random field optimization. The performance of the proposed method was demonstrated on 15 clinical datasets, preserving anatomical details and yielding superior results when compared with different reconstruction methods as visually and quantitatively assessed.
3D displacement field reconstruction on an irregular domain from planar tagged cardiac MR images

An important application of deformable motion estimation theory is the estimation of heart motion from tagged magnetic resonance image sequences. In tagged MR images, the heart appears with a spatially encoded pattern that moves with the tissue. The position of the tag pattern in each frame of the image sequence can be used to obtain sparse measurements of the heart's 3D displacement field. In this paper, we propose a method for estimating a dense displacement field from sparse displacement measurements that is based on a multidimensional stochastic model for the smoothness and divergence of the displacement field and uses the Fisher estimation framework. The main feature of this method is that the displacement field model and the resulting estimate equation are defined only on the irregular domain of the myocardium. Simulation results are presented that demonstrate the accuracy of our method and show the effect of the tag pattern on the estimation error.<<ETX>>
Improving magnetic resonance resolution with supervised learning

Despite ongoing improvements in magnetic resonance (MR) imaging (MRI), considerable clinical and, to a lesser extent, research data is acquired at lower resolutions. For example 1 mm isotropic acquisition of T1-weighted (T1-w) Magnetization Prepared Rapid Gradient Echo (MPRAGE) is standard practice, however T2-weighted (T2-w) - because of its longer relaxation times (and thus longer scan time) - is still routinely acquired with slice thicknesses of 2-5 mm and in-plane resolution of 2-3 mm. This creates obvious fundamental problems when trying to process T1-w and T2-w data in concert. We present an automated supervised learning algorithm to generate high resolution data. The framework is similar to the brain hallucination work of Rousseau, taking advantage of new developments in regression based image reconstruction. We present validation on phantom and real data, demonstrating the improvement over state-of-the-art super-resolution techniques.
A Hybrid Eulerian-Lagrangian Approach for Thickness, Correspondence, and Gridding of Annular Tissues

We present a novel approach to efficiently compute thickness, correspondence, and gridding of tissues between two simply connected boundaries. The solution of Laplace's equation within the tissue region provides a harmonic function whose gradient flow determines the correspondence trajectories going from one boundary to the other. The proposed method uses and expands upon two recently introduced techniques in order to compute thickness and correspondences based on these trajectories. Pairs of partial differential equations are efficiently computed within an Eulerian framework and combined with a Lagrangian approach so that correspondences trajectories are partially constructed when necessary. Examples are presented in order to compare the performance of this method with those of the pure Lagrangian and pure Eulerian approaches. Results show that the proposed technique takes advantage of both the speed of the Eulerian approach and the accuracy of the Lagrangian approach.
A Geometry-Driven Optical Flow Warping for Spatial Normalization of Cortical Surfaces

Spatial normalization is frequently used to map data to a standard coordinate system by removing intersubject morphological differences, thereby allowing for group analysis to be carried out. The work presented in this paper is motivated by the need for an automated cortical surface normalization technique that will automatically identify homologous cortical landmarks and map them to the same coordinates on a standard manifold. The geometry of a cortical surface is analyzed using two shape measures that distinguish the sulcal and gyral regions in a multiscale framework. A multichannel optical flow warping procedure aligns these shape measures between a reference brain and a subject brain, creating the desired normalization. The partial differential equation that carries out the warping is implemented in a Euclidean framework in order to facilitate a multiresolution strategy, thereby permitting large deformations between the two surfaces. The technique is demonstrated by aligning 33 normal cortical surfaces and showing both improved structural alignment in manually labeled sulci and improved functional alignment in positron emission tomography data mapped to the surfaces. A quantitative comparison between our proposed surface-based spatial normalization method and a leading volumetric spatial normalization method is included to show that the surface-based spatial normalization performs better in matching homologous cortical anatomies.
Exact 3D cone-beam reconstruction from projections obtained over a Wobble trajectory on a C-arm

We propose a source trajectory for cone-beam geometry that satisfies the conditions for exact 3D reconstruction in the largest sphere that remains non-truncated in the field of view of a given source detector combination. The source trajectory is a natural variation of the traditional circular source trajectory and is achieved by modulating the elevation of the source from the equatorial plane. We use a shift variant filtering approach in developing a filtered back-projection algorithm for robust, and computationally efficient volumetric reconstruction. The method is theoretically exact, and we show some results based upon simulation studies using a mathematical phantom.
Reconstruction of 3-D left ventricular motion from planar tagged cardiac MR images: an estimation theoretic approach

Magnetic resonance (MR) tagging has shown great potential for noninvasive measurement of the motion of a beating heart. In MR tagged images, the heart appears with a spatially encoded pattern that moves with the tissue. The position of the tag pattern in each frame of the image sequence can be used to obtain a measurement of the 3-D displacement field of the myocardium. The measurements are sparse, however, and interpolation is required to reconstruct a dense displacement field from which measures of local contractile performance such as strain can be computed. Here, the authors propose a method for estimating a dense displacement field from sparse displacement measurements. Their approach is based on a multidimensional stochastic model for the smoothness and divergence of the displacement field and the Fisher estimation framework. The main feature of this method is that both the displacement field model and the resulting estimate equation are defined only on the irregular domain of the myocardium. The authors' methods are validated on both simulated and in vivo heart data.
A 2D moving grid geometric deformable model

Geometric deformable models based on the level set method have become very popular. To overcome an inherent limitation in accuracy while maintaining computational efficiency, adaptive grid techniques using local grid refinement have been developed for use with these models. However, this strategy requires a very complex data structure, yields large numbers of contour points, and is inconsistent with our previously presented topology-preserving geometric deformable model (TGDM). In this paper, we incorporate an alternative adaptive grid technique called the moving grid method into the geometric deformable model framework. We find that it is simpler to implement than grid refinement, requiring no large, complex, hierarchical data structures. It also limits the number of contour vertices in the final contour and supports the incorporation of the topology-preserving constraint of TGDM. After presenting the algorithm, we demonstrate its performance using both simulated and real images.
Deformablemable Registration of CT Pelvis Images Using Mjolnir

Our recently published 3D-3D deformable image registration algorithm, Mjolnir (Ellingsen et al., 2006) was developed for inter-subject registration of MR brain images. Mjolnir is a hybrid registration method, where both anatomical features and image intensities are used to hierarchically align the images. In addition to the hierarchical scheme, the algorithm was implemented in a multi-resolution framework, which both reduces local minima and speeds up the registration process. In this paper an extension to this work is proposed, where Mjolnir has been adapted to register CT images of the pelvis. The main concepts of Mjolnir will be briefly described and the changes made to previous work explained. The algorithm was tested on CT images of the pelvis of 13 different subjects. Results indicate good registration accuracy however, further validation is needed
MR tag surface tracking using a spatio-temporal filter/interpolator

Magnetic resonance imaging provides the unique capability to produce artificial, high-contrast features called "tags" that are invaluable for tracking tissue motion. Each tag corresponds to an initially planar surface embedded in the tissue that deforms with tissue motion. By tracking tag surface deformation, quantitative analysis of tissue motion can be performed. Here, the authors present a method for tag surface tracking that applies specifically to tag surfaces embedded in the wall of the left ventricle. The method addresses two key issues: first, the full spatial extent of tag surfaces in 3-D space must be inferred from 2-D images, and second, within the images, noise leads to uncertainty in tag positions. The authors address these issues by framing tag surface tracking as an estimation problem given the observed image data. The estimates are obtained using a stochastic model of tag deformation and a recursive algorithm that simultaneously filters over time and smoothly interpolates between images.
An Improved Classifier Chain Algorithm for Multi-label Classification of Big Data Analysis

The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome this issue with a different strategy: Several times K-means algorithms are employed to get the correlations between labels and to confirm the order of binary classifiers. The algorithm ensure the right correlations be transmitted persistently as great as possible by improve the earlier predictions accuracy. The experimental results on the Reuters-21578 text chat data set and image data set show that the approach is efficient and appealing in most cases.
Using the Greenup, Powerup, and Speedup metrics to evaluate software energy efficiency

With recognizing power as a first-class citizen in the HPC community and the growth of software running on battery-driven devices, the need to evaluate software design based on the combined effects of energy and performance has become eminent. Despite of the numerous metrics to evaluate software performance, the study on how to evaluate software energy efficiency is still in its early stage. In this paper, we propose the Greenup, Powerup, and Speedup metrics (GPS-UP) to categorize software implementation and optimization efficiency. The GPSUP metrics transform the performance, power and energy of a program into a point on the GPS-UP software energy efficiency quadrant graph. We present eight categories of possible scenarios of software optimization, with examples on how to obtain them. Four categories are green (save energy), and four are red (waste energy). Moreover, we compare our metrics to existing metrics such as Energy Delay Product (EDP).
Voltage Assignment for Soft Real-Time Embedded Systems with Continuous Probability Distribution

Energy saving is critical to real-time embedded systems. In many embedded systems, some tasks contain conditional instructions or operations that could have different execution times for different inputs. Due to the uncertainties in execution time of these tasks, this paper models each varied execution time as a probabilistic random variable. We propose a practical algorithm to minimize the expected value of total energy consumption while satisfying the timing constraint with a guaranteed confidence probability for uniprocessor embedded systems with continuous probability distributions. The experimental results show that our approach achieves significant energy saving than previous work.