Measuring User Beliefs and Attitudes towards Conceptual Schemas: Tentative Factor and Structural Equation Model

Human factors research in conceptual modeling is scarce. Recently, quality assurance frameworks, methods and tools for conceptual schemas have received increased research attention, but the perception of quality by schema users has largely been ignored in this stream of research. This paper proposes a tentative model of user beliefs and attitudes towards the quality of conceptual schemas. The proposed model is original in the sense that it includes both perceived semantic quality and perceived pragmatic quality measures. The paper also presents a new measurement instrument for the perceived semantic quality of conceptual schemas. This instrument was used in a classroom experiment that tested the proposed user beliefs and attitudes model. It was shown that the perceived semantic quality of a schema is directly related to its perceived usefulness and perceived ease of use and indirectly to the user satisfaction with the schema

Deep learning pipeline for quality filtering of MRSI spectra.

With the rise of novel 3D magnetic resonance spectroscopy imaging (MRSI) acquisition protocols in clinical practice, which are capable of capturing a large number of spectra from a subject's brain, there is a need for an automated preprocessing pipeline that filters out bad-quality spectra and identifies contaminated but salvageable spectra prior to the metabolite quantification step. This work introduces such a pipeline based on an ensemble of deep-learning classifiers. The dataset consists of 36,338 spectra from one healthy subject and five brain tumor patients, acquired with an EPSI variant, which implemented a novel type of spectral editing named SLOtboom-Weng (SLOW) editing on a 7T MR scanner. The spectra were labeled manually by an expert into four classes of spectral quality as follows: (i) noise, (ii) spectra greatly influenced by lipid-related artifacts (deemed not to contain clinical information), (iii) spectra containing metabolic information slightly contaminated by lipid signals, and (iv) good-quality spectra. The AI model consists of three pairs of networks, each comprising a convolutional autoencoder and a multilayer perceptron network. In the classification step, the encoding half of the autoencoder is kept as a dimensionality reduction tool, while the fully connected layers are added to its output. Each of the three pairs of networks is trained on different representations of spectra (real, imaginary, or both), aiming at robust decision-making. The final class is assigned via a majority voting scheme. The F1 scores obtained on the test dataset for the four previously defined classes are 0.96, 0.93, 0.82, and 0.90, respectively. The arguably lower value of 0.82 was reached for the least represented class of spectra mildly influenced by lipids. Not only does the proposed model minimise the required user interaction, but it also greatly reduces the computation time at the metabolite quantification step (by selecting a subset of spectra worth quantifying) and enforces the display of only clinically relevant information

Automated MRI quantification of volumetric per-muscle fat fractions in the proximal leg of patients with muscular dystrophies

Muscular dystrophies (MD) are a class of rare genetic diseases resulting in progressive muscle weakness affecting specific muscle groups, depending on the type of disease. Disease progression is characterized by the gradual replacement of muscle tissue by fat, which can be assessed with fat-sensitive magnetic resonance imaging (MRI) and objectively evaluated by quantifying the fat fraction percentage (FF%) per muscle. Volumetric quantification of fat replacement over the full 3D extent of each muscle is more precise and potentially more sensitive than 2D quantification in few selected slices only, but it requires an accurate 3D segmentation of each muscle individually, which is time consuming when this has to be performed manually for a large number of muscles. A reliable, largely automated approach for 3D muscle segmentation is thus needed to facilitate the adoption of fat fraction quantification as a measure of MD disease progression in clinical routine practice, but this is challenging due to the variable appearance of the images and the ambiguity in the discrimination of the contours of adjacent muscles, especially when the normal image contrast is affected and diminished by the fat replacement. To deal with these challenges, we used deep learning to train AI-models to segment the muscles in the proximal leg from knee to hip in Dixon MRI images of healthy subjects as well as patients with MD. We demonstrate state-of-the-art segmentation results of all 18 muscles individually in terms of overlap (Dice score, DSC) with the manual ground truth delineation for images of cases with low fat infiltration (mean overall FF%: 11.3%; mean DSC: 95.3% per image, 84.4–97.3% per muscle) as well as with medium and high fat infiltration (mean overall FF%: 44.3%; mean DSC: 89.0% per image, 70.8–94.5% per muscle). In addition, we demonstrate that the segmentation performance is largely invariant to the field of view of the MRI scan, is generalizable to patients with different types of MD and that the manual delineation effort to create the training set can be drastically reduced without significant loss of segmentation quality by delineating only a subset of the slices

Imaging ischemic and reperfusion injury in acute myocardial infarction putting the pieces together with CMR

rom the a Department of Imaging and Pathology, KU Leuven – University of Leuven, Leuven, Belgium; b Lab on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven – University of Leuven, Leuven, Belgium; c Life and Health Sciences Research Institute/Biomaterials, Biodegradables and Biomimetics Research Group — Portugal Government Associate Laboratory, Braga/Guimarães, Portugal; d Instituto de Engenharia Mecânica e Gestão Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal; and the e Medical Imaging Research Center, ESAT-PSI, Processing Speech and Images (PSI), Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium. Dr. Morais has received funding for his PhD scholarship (FCT — Fundacão para a Ciência e a Tecnologia, Portugal, for funding support through the Programa Operacional Capital Humano in the scope of the PhD grant SFRH/BD/95438/2013). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.info:eu-repo/semantics/publishedVersio

Elastic image registration versus speckle tracking for 2-D myocardial motion estimation: a direct comparison in vivo

Despite the availability of multiple solutions for assessing myocardial strain by ultrasound, little is currently known about the relative performance of the different methods. In this study, we sought to contrast two strain estimation techniques directly (speckle tracking and elastic registration) in an in vivo setting by comparing both to a gold standard reference measurement. In five open-chest sheep instrumented with ultrasonic microcrystals, 2-D images were acquired with a GE Vivid7 ultrasound system. Radial (epsilon(RR)) , longitudinal (epsilon(LL)) , and circumferential strain (epsilon(CC)) were estimated during four inotropic stages: at rest, during esmolol and dobutamine infusion, and during acute ischemia. The correlation of the end-systolic strain values of a well-validated speckle tracking approach and an elastic registration method against sonomicrometry were comparable for epsilon(LL) (r = 0.70 versus r = 0.61, respectively; p = 0.32) and epsilon(CC) (r = 0.73 versus r = 0.80 respectively; p = 0.31). However, the elastic registration method performed considerably better for epsilon(RR) (r = 0.64 versus r = 0.85 respectively; p = 0.09). Moreover, the bias and limits of agreement with respect to the reference strain estimates were statistically significantly smaller in this direction (p < 0.001). This could be related to regularization which is imposed during the motion estimation process as opposed to an a posteriori regularization step in the speckle tracking method. Whether one method outperforms the other in detecting dysfunctional regions remains the topic of future research

3D simulations of AGB stellar winds -- II. Ray-tracer implementation and impact of radiation on the outflow morphology

Stars with an initial mass below ~ 8 Msun evolve through the asymptotic giant branch (AGB) phase, during which they develop strong stellar winds. Recent observations have revealed significant morphological complexities in their outflows, most likely caused by a companion. We study the impact of the radiation force on such companion-perturbed AGB outflows. We present the implementation of a ray tracer for radiative transfer in smoothed particle hydrodynamics (SPH) and compared four different descriptions of radiative transfer: the free-wind, the geometrical, the Lucy, and the attenuation approximation. For both low and high mass-loss rates, the velocity profile of the outflow is modified when going from the free-wind to the geometrical approximation, also resulting in a different morphology. In the case of a low mass-loss rate, the effect of the Lucy and attenuation approximation is negligible due to the low densities but morphological differences appear in the high mass-loss rate regime. By comparing the radiative equilibrium temperature and radiation force to full 3D radiative transfer, we show that the Lucy approximation works best. Although, close to the companion, artificial heating occurs and it fails to simulate the shadow cast by the companion. The attenuation approximation produces a lower equilibrium temperature and weaker radiation force, but it produces the shadow cast by the companion. From the predictions of the 3D radiative transfer, we also conclude that a radially directed radiation force is a reasonable assumption. The radiation force thus plays a critical role in dust-driven AGB winds, impacting the velocity profile and morphological structures. For low mass-loss rates, the geometrical approximation suffices, while high mass-loss rates require a more rigorous method, where the Lucy approximation provides the most accurate results although not accounting for all effects