Automated region growing-based segmentation for trabecular bone structure in fresh-frozen human wrist specimens.

Bone strength depends on both mineral content and bone structure. Measurements of bone microstructure on specimens can be performed by micro-CT. In vivo measurements are reliably performed by high-resolution peripheral computed tomography (HR-pQCT) using dedicated software. In previous studies from our research group, trabecular bone properties on CT data of defatted specimens from many different CT devices have been analyzed using an Automated Region Growing (ARG) algorithm-based code, showing strong correlations to micro-CT.The aim of the study was to validate the possibility of segmenting and measuring trabecular bone structure from clinical CT data of fresh-frozen human wrist specimens. Data from micro-CT was used as reference. The hypothesis was that the ARG-based in-house built software could be used for such measurements.HR-pQCT image data at two resolutions (61 and 82 µm isotropic voxels) from 23 fresh-frozen human forearms were analyzed. Correlations to micro-CT were strong, varying from 0.72 to 0.99 for all parameters except trabecular termini and nodes. The bone volume fraction had correlations varying from 0.95 to 0.98 but was overestimated compared to micro-CT, especially at the lower resolution. Trabecular separation and spacing were the most stable parameters with correlations at 0.80-0.97 and mean values in the same range as micro-CT.Results from this in vitro study show that an ARG-based software could be used for segmenting and measuring 3D trabecular bone structure from clinical CT data of fresh-frozen human wrist specimens using micro-CT data as reference. Over-and underestimation of several of the bone structure parameters must however be taken into account.

Randomized iterative spherical-deconvolution informed tractogram filtering.

Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable for judging the compliance of individual streamlines with the acquired data since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo-ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of complying and non-complying streamlines with the acquired data with an accuracy above 80%.

Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration.

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2  ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.

Reinforcement Learning-Based Autonomous Driving at Intersections in CARLA Simulator.

Intersections are considered one of the most complex scenarios in a self-driving framework due to the uncertainty in the behaviors of surrounding vehicles and the different types of scenarios that can be found. To deal with this problem, we provide a Deep Reinforcement Learning approach for intersection handling, which is combined with Curriculum Learning to improve the training process. The state space is defined by two vectors, containing adversaries and ego vehicle information. We define a features extractor module and an actor-critic approach combined with Curriculum Learning techniques, adding complexity to the environment by increasing the number of vehicles. In order to address a complete autonomous driving system, a hybrid architecture is proposed. The operative level generates the driving commands, the strategy level defines the trajectory and the tactical level executes the high-level decisions. This high-level decision system is the main goal of this research. To address realistic experiments, we set up three scenarios: intersections with traffic lights, intersections with traffic signs and uncontrolled intersections. The results of this paper show that a Proximal Policy Optimization algorithm can infer ego vehicle-desired behavior for different intersection scenarios based only on the behavior of adversarial vehicles.

Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator.

Autonomous vehicles are the near future of the automobile industry. However, until they reach Level 5, humans and cars will share this intermediate future. Therefore, studying the transition between autonomous and manual modes is a fascinating topic. Automated vehicles may still need to occasionally hand the control to drivers due to technology limitations and legal requirements. This paper presents a study of driver behaviour in the transition between autonomous and manual modes using a CARLA simulator. To our knowledge, this is the first take-over study with transitions conducted on this simulator. For this purpose, we obtain driver gaze focalization and fuse it with the road's semantic segmentation to track to where and when the user is paying attention, besides the actuators' reaction-time measurements provided in the literature. To track gaze focalization in a non-intrusive and inexpensive way, we use a method based on a camera developed in previous works. We devised it with the OpenFace 2.0 toolkit and a NARMAX calibration method. It transforms the face parameters extracted by the toolkit into the point where the user is looking on the simulator scene. The study was carried out by different users using our simulator, which is composed of three screens, a steering wheel and pedals. We distributed this proposal in two different computer systems due to the computational cost of the simulator based on the CARLA simulator. The robot operating system (ROS) framework is in charge of the communication of both systems to provide portability and flexibility to the proposal. Results of the transition analysis are provided using state-of-the-art metrics and a novel driver situation-awareness metric for 20 users in two different scenarios.

Structural and Rheological Properties of Pectins Extracted from Industrial Sugar Beet By-Products.

In this work, the efficient extraction of pectin from sugar beet by-products (pressed, ensiled and dried pulp), by using an acid method or a commercial cellulose, is accomplished. The extraction method had an impact on the pectin monomeric composition, mainly in xylose, arabinose, and galacturonic acid content, as determined by GC-FID. FTIR and SEC analyses allowed the determination of similar degrees of methoxylation and molecular weights, respectively, in the extracted pectins. The acid extraction of pectin in the ensiled by-product led to the highest yield (19%) with a galacturonic acid content of 46%, whereas the application of the enzymatic extraction method resulted in a lower yield (13%) but higher galacturonic acid content (72%). Moreover, the stability in aqueous solution as well as the emulsifying activity index was higher for pectin extracted by the acid method, whereas the viscosity was higher in pectin extracted by the enzymatic method. To the best of our knowledge, this is the first study analyzing the physicochemical properties and exploring the potential reuse of ensiled and dried by-products from sugar beet industry for the extraction of pectin to be further used in the food and pharmaceutical areas.

Preparation of citrus pectin gels by power ultrasound and its application as an edible coating in strawberries.

BACKGROUND: Pectin is heteropolysaccharide found in cell walls originating mainly from by-products, as well as citrus peels, apple and sugar beet pulp, and presenting biological and techno-functional properties. In the present study, a general and structural characterisation of industrial citrus pectins was performed together with a study of impact of power ultrasound (US) on their rheological properties, with the aim of using them as edible coatings for fresh strawberries. RESULTS: The results obtained indicated that pure pectin showed a methylesterification degree greater than 50% and galacturonic acid content > 65%, supporting its consideration as additive E-440; such conditions were not achieved in pectin with sugar addition. Furthermore, in the rheological study, pectin gels showed a non-Newtonian flow and pseudoplastic behaviour and presented different viscosity ranges depending on the preparation methods, including power US. Gels were used as edible coatings for fresh strawberries aiming to improve their quality during storage over a period of 5 days, controlling quality characteristics such as humidity loss, acidity and colour parameters (L*, a*, b*, C, h°, ΔE). CONCLUSION: The results obtained demonstrate that US treatments give rise to pectin gels that can improve the quality of strawberries over their lifetime. © 2018 Society of Chemical Industry.

Quantitative fat and R2* mapping in vivo to measure lipid-rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis.

PURPOSE: The aim of this work was to quantify the extent of lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in atherosclerotic plaques. METHODS: Patients scheduled for carotid endarterectomy underwent four-point Dixon and T1-weighted magnetic resonance imaging (MRI) at 3 Tesla. Fat and R2* maps were generated from the Dixon sequence at the acquired spatial resolution of 0.60 × 0.60 × 0.70 mm voxel size. MRI and three-dimensional (3D) histology volumes of plaques were registered. The registration matrix was applied to segmentations denoting LRNC and IPH in 3D histology to split plaque volumes in regions with and without LRNC and IPH. RESULTS: Five patients were included. Regarding volumes of LRNC identified by 3D histology, the average fat fraction by MRI was significantly higher inside LRNC than outside: 12.64 ± 0.2737% versus 9.294 ± 0.1762% (mean ± standard error of the mean [SEM]; P < 0.001). The same was true for IPH identified by 3D histology, R2* inside versus outside IPH was: 71.81 ± 1.276 s-1 versus 56.94 ± 0.9095 s-1 (mean ± SEM; P < 0.001). There was a strong correlation between the cumulative fat and the volume of LRNC from 3D histology (R2 = 0.92) as well as between cumulative R2* and IPH (R2 = 0.94). CONCLUSION: Quantitative mapping of fat and R2* from Dixon MRI reliably quantifies the extent of LRNC and IPH. Magn Reson Med 78:285-296, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Planning low-carbon electricity systems under uncertainty considering operational flexibility and smart grid technologies.

Electricity grid operators and planners need to deal with both the rapidly increasing integration of renewables and an unprecedented level of uncertainty that originates from unknown generation outputs, changing commercial and regulatory frameworks aimed to foster low-carbon technologies, the evolving availability of market information on feasibility and costs of various technologies, etc. In this context, there is a significant risk of locking-in to inefficient investment planning solutions determined by current deterministic engineering practices that neither capture uncertainty nor represent the actual operation of the planned infrastructure under high penetration of renewables. We therefore present an alternative optimization framework to plan electricity grids that deals with uncertain scenarios and represents increased operational details. The presented framework is able to model the effects of an array of flexible, smart grid technologies that can efficiently displace the need for conventional solutions. We then argue, and demonstrate via the proposed framework and an illustrative example, that proper modelling of uncertainty and operational constraints in planning is key to valuing operationally flexible solutions leading to optimal investment in a smart grid context. Finally, we review the most used practices in power system planning under uncertainty, highlight the challenges of incorporating operational aspects and advocate the need for new and computationally effective optimization tools to properly value the benefits of flexible, smart grid solutions in planning. Such tools are essential to accelerate the development of a low-carbon energy system and investment in the most appropriate portfolio of renewable energy sources and complementary enabling smart technologies.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'.

Trabecular bone structure parameters from 3D image processing of clinical multi-slice and cone-beam computed tomography data.

OBJECTIVE: Bone strength depends on both mineral content and bone structure. The aim of this in vitro study was to develop a method of quantitatively assessing trabecular bone structure by applying three-dimensional image processing to data acquired with multi-slice and cone-beam computed tomography using micro-computed tomography as a reference. MATERIALS AND METHODS: Fifteen bone samples from the radius were examined. After segmentation, quantitative measures of bone volume, trabecular thickness, trabecular separation, trabecular number, trabecular nodes, and trabecular termini were obtained. RESULTS: The clinical machines overestimated bone volume and trabecular thickness and underestimated trabecular nodes and number, but cone-beam CT to a lesser extent. Parameters obtained from cone beam CT were strongly correlated with µCT, with correlation coefficients between 0.93 and 0.98 for all parameters except trabecular termini. CONCLUSIONS: The high correlation between cone-beam CT and micro-CT suggest the possibility of quantifying and monitoring changes of trabecular bone microarchitecture in vivo using cone beam CT.

[Perception of medical students about formative assessments during clinical courses]. / Utilidad de la evaluación formativa en cursos clínicos integrativos en estudiantes de pregrado de medicina.

BACKGROUND: Formative evaluation is a range of formal assessment employed by professors during the teaching process in order to modify curriculum activities, to improve student attainment. For students, it is helpful to evaluate their learning process. Although recommended, it is seldom used. AIM: To evaluate the perception and performance of medical students subjected to formative assessments during an integrative clinical course. MATERIAL AND METHODS: Fourth year medical students that participated in a multiple choice formative assessment, similar to the final exam during 2007 and 2008, responded a survey about the usefulness and quality of such assessment. Student achievement was expressed as the percentage of correct answers of the tests. RESULTS: The formative assessment was answered by 99% of students. In 2007 97% of students considered the experience as excellent or very good and 92% evaluated it as useful or very useful. During 2008 the figures were 89% and 79%. The students outlined that this assessment oriented their study, allowed them to discover their weaknesses and have a perception of the degree of difficulty of the final exam. Over 90% of students that took the formative evaluation, improved their academic achievement. CONCLUSIONS: Formative assessments are well evaluated by medical students and improve their academic achievement.

Analysis of the Rheological Properties of Natural Hydraulic Lime-Based Suspensions for Sustainable Construction and Heritage Conservation.

Natural hydraulic lime (NHL)-based binders play a crucial role in preserving cultural heritage structures, ensuring integrity and longevity. Beyond traditional uses, these binders exhibit potential for integration into both non-structural and structural components, being compatible with innovative manufacturing processes such as digital fabrication. Meticulously designed grouts, with applicability in their fresh and hardened states, are essential for heritage stability. This study explores the relationships between mineral additions, chemical admixtures, and lime for grout formulations, aiming to advance our understanding and inform the optimization of materials for heritage restoration. Key questions include the influence of natural volcanic pozzolan (NVP) and metakaolin (MK) on rheology and the impact of varying ratios of superplasticizer on NHL-based grout's rheological behavior. This systematic evaluation of rheological parameters aims to innovate mix designs, expanding NHL-based binders' applicability in construction and science. Our hypotheses suggest that well-designed lime grout formulations, incorporating NVP and MK, can enhance rheological properties, addressing challenges in sustainable construction and heritage conservation. This research provides valuable insights for optimizing lime-based materials, fostering advancements in heritage restoration, and promoting wider NHL-based binder adoption in diverse construction applications.

Asymmetry in Cortical Thickness of the Heschl's Gyrus in Unilateral Ear Canal Atresia.

HYPOTHESIS: Unilateral congenital conductive hearing impairment in ear canal atresia leads to atrophy of the gray matter of the contralateral primary auditory cortex or changes in asymmetry pattern if left untreated in childhood. BACKGROUND: Unilateral ear canal atresia with associated severe conductive hearing loss results in deteriorated sound localization and difficulties in understanding of speech in a noisy environment. Cortical atrophy in the Heschl's gyrus has been reported in acquired sensorineural hearing loss but has not been studied in unilateral conductive hearing loss. METHODS: We obtained T1w and T2w FLAIR MRI data from 17 subjects with unilateral congenital ear canal atresia and 17 matched controls. Gray matter volume and thickness were measured in the Heschl's gyrus using Freesurfer. RESULTS: In unilateral congenital ear canal atresia, Heschl's gyrus exhibited cortical thickness asymmetry (right thicker than left, corrected p = 0.0012, mean difference 0.25 mm), while controls had symmetric findings. Gray matter volume and total thickness did not differ from controls with normal hearing. CONCLUSION: We observed cortical thickness asymmetry in congenital unilateral ear canal atresia but no evidence of contralateral cortex atrophy. Further research is needed to understand the implications of this asymmetry on central auditory processing deficits.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods: We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion: We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

Generalizing the mean intercept length tensor for gray-level images.

PURPOSE: The mean intercept length tensor is the most used technique to estimate microstructure orientation and anisotropy of trabecular bone. This paper proposes an efficient extension of this technique to gray-scale images based on a closed formulation of the mean intercept length tensor and a generalization using different angular convolution kernels. METHODS: First, the extended Gaussian image is computed for the binary or gray-scale image. Second, the intercepts are computed for all possible orientations through an angular convolution with the half-cosine function. Finally, the tensor is computed by means of the covariance matrix. The complexity of the method is O(n + m) in contrast with O(nm) of traditional implementations, where n is the number of voxels in the image and m is the number of orientations used in the computations. The method is generalized by applying other angular convolution kernels instead of the half-cosine function. As a result, the anisotropy of the tensor can be controlled while keeping the eigenvectors intact. RESULTS: The proposed extension to gray-scale yields accurate results for reliable computations of the extended Gaussian image and, unlike the traditional methodology, is not affected by artifacts generated by discretizations during the sampling of different orientations. CONCLUSIONS: Experiments show that the computations on both binary and gray-scale images are correlated, and that computations in gray-scale are more robust, enabling the use of the mean intercept length tensor to clinical examinations of trabecular bone. The use of kernels based on the von Mises-Fisher distribution is promising as the anisotropy can be adjusted with a parameter in order to improve its power to predict mechanical properties of trabecular bone.

Surgical aspects of donor hand recovery for transplantation.

The purpose of this article is to share our institution's experience in optimizing the suitability of composite donor tissue for use in hand transplantation. The centerpiece of this process includes procurement techniques, preservation and timing issues, and anatomical matching. Recovery of the donor hand must proceed in an efficient, organized, and expedient manner. Proper timing of the donor operation not only ensures the quality of donor tissue and outcome for the hand recipient, but also allows surgeons recovering other organs to obtain high quality tissue for those recipients. Timing remains a critical factor in preserving tissue after removal from the donor. We will also consider the factors of temperature and preservation solution during transport.

Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients.

Objective: Survival Rate Prediction (SRP) is a valuable tool to assist in the clinical diagnosis and treatment planning of lung cancer patients. In recent years, deep learning (DL) based methods have shown great potential in medical image processing in general and SRP in particular. This study proposes a fully-automated method for SRP from computed tomography (CT) images, which combines an automatic segmentation of the tumor and a DL-based method for extracting rotational-invariant features. Methods: In the first stage, the tumor is segmented from the CT image of the lungs. Here, we use a deep-learning-based method that entails a variational autoencoder to provide more information to a U-Net segmentation model. Next, the 3D volumetric image of the tumor is projected onto 2D spherical maps. These spherical maps serve as inputs for a spherical convolutional neural network that approximates the log risk for a generalized Cox proportional hazard model. Results: The proposed method is compared with 17 baseline methods that combine different feature sets and prediction models using three publicly-available datasets: Lung1 (n=422), Lung3 (n=89), and H&N1 (n=136). We observed comparable C-index scores compared to the best-performing baseline methods in a 5-fold cross-validation on Lung1 (0.59 ± 0.03 vs. 0.62 ± 0.04). In comparison, it slightly outperforms all methods in inter-data set evaluation (0.64 vs. 0.63). The best-performing method from the first experiment reduced its performance to 0.61 and 0.62 for Lung3 and H&N1, respectively. Discussion: The experiments suggest that the performance of spherical features is comparable with previous approaches, but they generalize better when applied to unseen datasets. That might imply that orientation-independent shape features are relevant for SRP. The performance of the proposed method was very similar, using manual and automatic segmentation methods. This makes the proposed model useful in cases where expert annotations are not available or difficult to obtain.

Vascularized Bone Grafting for the Treatment of Capitate Avascular Necrosis.

Avascular necrosis (AVN) of the capitate bone is a rare condition and it can be related to major trauma or idiopathy. Different treatments are available including soft tissue interposition and intercarpal arthrodesis including lunocapitate, scaphocapitate, four corner, and carpometacarpal fusions. Other surgical options are resection of the proximal pole and revascularization procedures. The main purpose of this article is to present two cases of AVN of the capitate treated with a revascularization procedure using the 4th-5th extensor compartment artery (4th-5th ECA). Two female patients with capitate AVN are reported with an average age of 30.5 years. Both cases were classified as type-I according to Milliez classification. The major complaint in each case was wrist pain that increased during activity. In both cases there was no history of trauma, smoking, diabetes, or hematologic diseases. Both patients had a diminished range of motion, grip, and strength. The definitive diagnosis was made with magnetic resonance imaging. Both patients underwent treatment revascularization of the capitate using a vascularized bone graft based on the 4th-5th ECA. At average follow-up of 12 months, each patient had improved with regards to pain and had increased grip strength. The literature does not describe a specific algorithm treatment for capitate AVN. We recommend revascularization of the capitate using the 4th-5th ECA in type-I Milliez classification in young patients without signs of carpal collapse.

Automatic segmentation of the core of the acoustic radiation in humans.

Introduction: Acoustic radiation is one of the most important white matter fiber bundles of the human auditory system. However, segmenting the acoustic radiation is challenging due to its small size and proximity to several larger fiber bundles. TractSeg is a method that uses a neural network to segment some of the major fiber bundles in the brain. This study aims to train TractSeg to segment the core of acoustic radiation. Methods: We propose a methodology to automatically extract the acoustic radiation from human connectome data, which is both of high quality and high resolution. The segmentation masks generated by TractSeg of nearby fiber bundles are used to steer the generation of valid streamlines through tractography. Only streamlines connecting the Heschl's gyrus and the medial geniculate nucleus were considered. These streamlines are then used to create masks of the core of the acoustic radiation that is used to train the neural network of TractSeg. The trained network is used to automatically segment the acoustic radiation from unseen images. Results: The trained neural network successfully extracted anatomically plausible masks of the core of the acoustic radiation in human connectome data. We also applied the method to a dataset of 17 patients with unilateral congenital ear canal atresia and 17 age- and gender-paired controls acquired in a clinical setting. The method was able to extract 53/68 acoustic radiation in the dataset acquired with clinical settings. In 14/68 cases, the method generated fragments of the acoustic radiation and completely failed in a single case. The performance of the method on patients and controls was similar. Discussion: In most cases, it is possible to segment the core of the acoustic radiations even in images acquired with clinical settings in a few seconds using a pre-trained neural network.

Probabilistic Assessment of the Dynamic Viscosity of Self-Compacting Steel-Fiber Reinforced Concrete through a Micromechanical Model.

This article develops a probabilistic approach to a micromechanical model to calculate the dynamic viscosity in self-compacting steel-fiber reinforced concrete (SCSFRC), which implies a paradigm shift in the approach of the deterministic models used. It builds on a previous work by the authors in which Bayesian analysis is applied to rheological micromechanical models in cement paste, self-compacting mortar, and self-compacting concrete. As a consequence of the varied characteristics of the particles in these suspensions (in terms of materials, shapes, size distributions, etc.), as well as their random nature, it seems appropriate to study these systems with probabilistic models. The Bayesian analysis, thorough Markov Chain Monte Carlo and Gibbs Sampling methods, allows the conversion of parametric-deterministic models into parametric-probabilistic models, which results in enrichment in engineering and science. The incorporation of steel fibers requires a new term in the model to account for their effect on the dynamic viscosity of SCSFRC, and this new term is also treated here with the Bayesian approach. The paper uses an extensive collection of experimental data to obtain the probability density functions of the parameters for assessing the dynamic viscosity in SCSFRC. The results obtained with these parameters' distributions are much better than those calculated with the theoretical values of the parameters, which indicates that Bayesian methods are appropriated to respond to questions in complex systems with complex models.