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Highlights • Method to detect and measure ablation gaps after pulmonary vein isolation. • No standard definition of the gaps and analysis typically done by visual inspection. • We provide a standardized definition of a gap in the neighborhood of the veins. • Also a method, based on graph theory, to detect the gaps and quantify their extent. • We use a decision fusion rule combining the results of different scar segmentations. Graphical abstract Abstract Pulmonary vein isolation (PVI) is a common procedure for the treatment of atrial fibrillation (AF) since the initial trigger for AF frequently originates in the pulmonary veins. A successful isolation produces a continuous lesion (scar) completely encircling the veins that stops activation waves from propagating to the atrial body. Unfortunately, the encircling lesion is often incomplete, becoming a combination of scar and gaps of healthy tissue. These gaps are potential causes of AF recurrence, which requires a redo of the isolation procedure. Late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) is a non-invasive method that may also be used to detect gaps, but it is currently a time-consuming process, prone to high inter-observer variability. In this paper, we present a method to semi-automatically identify and quantify ablation gaps. Gap quantification is performed through minimum path search in a graph where every node is a scar patch and the edges are the geodesic distances between patches. We propose the Relative Gap Measure (RGM) to estimate the percentage of gap around a vein, which is defined as the ratio of the overall gap length and the total length of the path that encircles the vein. Additionally, an advanced version of the RGM has been developed to integrate gap quantification estimates from different scar segmentation techniques into a single figure-of-merit. Population-based statistical and regional analysis of gap distribution was performed using a standardised parcellation of the left atrium. We have evaluated our method on synthetic and clinical data from 50 AF patients who underwent PVI with radiofrequency ablation. The population-based analysis concluded that the left superior PV is more prone to lesion gaps while the left inferior PV tends to have less gaps (p <.05 in both cases), in the processed data. This type of information can be very useful for the optimization and objective assessment of PVI interventions. [ABSTRACT FROM AUTHOR]

The specialised conducting tissues present in the ventricles are responsible for the fast distribution of the electrical impulse from the atrio-ventricular node to regions in the subendocardial myocardium. Characterisation of anatomical features of the specialised conducting tissues in the ventricles is highly challenging, in particular its most distal section, which is connected to the working myocardium via Purkinje-myocardial junctions. The goal of this work is to characterise the architecture of the distal section of the Purkinje network by differentiating Purkinje cells from surrounding tissue, performing a segmentation of Purkinje fibres at cellular scale, and mathematically describing its morphology and interconnections. Purkinje cells from rabbit hearts were visualised by confocal microscopy using wheat germ agglutinin labelling. A total of 16 3D stacks including labeled Purkinje cells were collected, and semi-automatically segmented. State-of-the-art graph metrics were applied to estimate regional and global features of the Purkinje network complexity. Two types of cell types, tubular and star-like, were characterised from 3D segmentations. The analysis of 3D imaging data confirms the previously suggested presence of two types of Purkinje-myocardium connections, a 2D interconnection sheet and a funnel one, in which the narrow side of a Purkinje fibre connect progressively to muscle fibres. The complex network analysis of interconnected Purkinje cells showed no small-world connectivity or assortativity properties. These results might help building more realistic computational PK systems at high resolution levels including different cell configurations and shapes. Better knowledge on the organisation of the network might help in understanding the effects that several treatments such as radio-frequency ablation might have when the PK system is disrupted locally. [ABSTRACT FROM AUTHOR]

Purpose: In this article, the authors studied the feasibility of estimating regional mechanical properties in cerebral aneurysms, integrating information extracted from imaging and physiological data with generic computational models of the arterial wall behavior. Methods: A data assimilation framework was developed to incorporate patient-specific geometries into a given biomechanical model, whereas wall motion estimates were obtained from applying registration techniques to a pair of simulated MR images and guided the mechanical parameter estimation. A simple incompressible linear and isotropic Hookean model coupled with computational fluid-dynamics was employed as a first approximation for computational purposes. Additionally, an automatic clustering technique was developed to reduce the number of parameters to assimilate at the optimization stage and it considerably accelerated the convergence of the simulations. Several in silico experiments were designed to assess the influence of aneurysm geometrical characteristics and the accuracy of wall motion estimates on the mechanical property estimates. Hence, the proposed methodology was applied to six real cerebral aneurysms and tested against a varying number of regions with different elasticity, different mesh discretization, imaging resolution, and registration configurations. Results: Several in silico experiments were conducted to investigate the feasibility of the proposed workflow, results found suggesting that the estimation of the mechanical properties was mainly influenced by the image spatial resolution and the chosen registration configuration. According to the in silico experiments, the minimal spatial resolution needed to extract wall pulsation measurements with enough accuracy to guide the proposed data assimilation framework was of 0.1 mm. Conclusions: Current routine imaging modalities do not have such a high spatial resolution and therefore the proposed data assimilation framework cannot currently be used on in vivo data to reliably estimate regional properties in cerebral aneurysms. Besides, it was observed that the incorporation of fluid-structure interaction in a biomechanical model with linear and isotropic material properties did not have a substantial influence in the final results. [ABSTRACT FROM AUTHOR]

Abstract: The evaluation of atrophy quantification methods based on magnetic resonance imaging have been usually hindered by the lack of realistic gold standard data against which to judge these methods or to help refine them. Recently [Camara, O., Schweiger, M., Scahill, R., Crum, W., Sneller, B., Schnabel, J., Ridgway, G., Cash, D., Hill, D., Fox, N., 2006. Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE Trans. Med.l Imaging 25, 1417–1430], we presented a technique in which atrophy is realistically simulated in different tissue compartments or neuroanatomical structures with a phenomenological model. In this study, we have generated a cohort of realistic simulated Alzheimer''s disease (AD) images with known amounts of atrophy, mimicking a set of 19 real controls and 27 probable AD subjects, with an improved version of our atrophy simulation methodology. This database was then used to assess the accuracy of several well-known computational anatomy methods which provide global (BSI and SIENA) or local (Jacobian integration) estimates of longitudinal atrophy in brain structures using MR images. SIENA and BSI results correlated very well with gold standard data (Pearson coefficient of 0.962 and 0.969 respectively), achieving small mean absolute differences with respect to the gold standard (percentage change from baseline volume): BSI of 0.23%±0.26%; SIENA of 0.22%±0.28%. Jacobian integration was guided by both fluid and FFD-based registration techniques and resulting deformation fields and associated Jacobians were compared, region by region, with gold standard ones. The FFD-based technique outperformed the fluid one in all evaluated structures (mean absolute differences from the gold standard in percentage change from baseline volume): whole brain, FFD=0.31%, fluid=0.58%; lateral ventricles, FFD=0.79%; fluid=1.45%; left hippocampus, FFD=0.82%; fluid=1.42%; right hippocampus, FFD=0.95%; fluid=1.62%. The largest errors for both local techniques occurred in the sulcal CSF (FFD=2.27%; fluid=3.55%) regions. For large structures such as the whole brain, these mean absolute differences, relative to the applied atrophy, represented similar percentages for the BSI, SIENA and FFD techniques (controls/patients): BSI, 51.99%/16.36%; SIENA, 62.34%/21.59%; FFD, 41.02%/24.95%. For small structures such as the hippocampi, these percentages were larger, especially for controls where errors were approximately equal to the small applied changes (controls/patients): FFD, 92.82%/43.61%. However, these apparently large relative errors have not prevented the global or hippocampal measures from finding significant group separation in our study. The evaluation framework presented here will help in quantifying whether the accuracy of future methodological developments is sufficient for analysing change in smaller or less atrophied local brain regions. Results obtained in our experiments with realistic simulated data confirm previously published estimates of accuracy for both evaluated global techniques. Regarding Jacobian Integration methods, the FFD-based one demonstrated promising results and potential for being used in clinical studies alongside (or in place of) the more common global methods. The generated gold standard data has also allowed us to identify some stages and sets of parameters in the evaluated techniques – the brain extraction step in the global techniques and the number of multi-resolution levels and the stopping criteria in the registration-based methods – that are critical for their accuracy. [Copyright &y& Elsevier]

The aim of this paper is to develop a registration methodology in order to combine anatomical and functional information provided by thoracic/abdominal computed tomography (CT) and whole-body positron emission tomography (PET) images. The proposed procedure is based on the incorporation of prior anatomical information in an intensity-based nonrigid registration algorithm. This incorporation is achieved in an explicit way, initializing the intensity-based registration stage with the solution obtained by a nonrigid registration of corresponding anatomical structures. A segmentation algorithm based on a hierarchically ordered set of anatomy-specific rules is used to obtain anatomical structures in CT and emission PET scans. Nonrigid deformations are modeled in both registration stages by means of free-form deformations, the optimization of the control points being achieved by means of an original vector field-based approach instead of the classical gradient-based techniques, considerably reducing the computational time of the structure registration stage. We have applied the proposed methodology to 38 sets of images (33 provided by standalone machines and five by hybrid systems) and an assessment protocol has been developed to furnish a qualitative evaluation of the algorithm performance. [ABSTRACT FROM AUTHOR]

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefacts is also presented. Cross-sectional and longitudinal sets of simulated data are shown and a visual classification protocol has been used by experts to rate real and simulated scans according to their degree of atrophy. Results confirm the potential of the proposed methodology. [ABSTRACT FROM AUTHOR]

Measures of overlap of labelled regions of images, such as the Dice and Tanimoto coefficients, have been extensively used to evaluate image registration and segmentation algorithms. Modern studies can include multiple labels defined on multiple images yet most evaluation schemes report one overlap per labelled region, simply averaged over multiple images. In this paper, common overlap measures are generalized to measure the total overlap of ensembles of labels defined on multiple test images and account for fractional labels using fuzzy set theory. This framework allows a single "figure-of-merit" to be reported which summarises the results of a complex experiment by image pair, by label or overall. A complementary measure of error, the overlap distance, is defined which captures the spatial extent of the nonoverlapping part and is related to the Hausdorff distance computed on grey level images. The generalized overlap measures are validated on synthetic images for which the overlap can be computed analytically and used as similarity measures in nonrigid registration of three-dimensional magnetic resonance imaging (MRI) brain images. Finally, a pragmatic segmentation ground truth is constructed by registering a magnetic resonance atlas brain to 20 individual scans, and used with the overlap measures to evaluate publicly available brain segmentation algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a general framework to integrate a new type of constraints, based on spatial relations, in deformable models. In the proposed approach, spatial relations are represented as fuzzy subsets of the image space and incorporated in the deformable model as a new external force. Three methods to construct an external force from a fuzzy set representing a spatial relation are introduced and discussed. This framework is then used to segment brain subcortical structures in magnetic resonance images (MRI). A training step is proposed to estimate the main parameters defining the relations. The results demonstrate that the introduction of spatial relations in a deformable model can substantially improve the segmentation of structures with low contrast and ill-defined boundaries. [Copyright &y& Elsevier]

Atrial fibrillation (AF) poses a significant risk of stroke due to thrombus formation, which primarily occurs in the left atrial appendage (LAA). Medical image‐based computational fluid dynamics (CFD) simulations can provide valuable insight into patient‐specific hemodynamics and could potentially enhance personalized assessment of thrombus risk. However, the importance of accurately representing the left atrial (LA) wall dynamics has not been fully resolved. In this study, we compared four modeling scenarios; rigid walls, a generic wall motion based on a reference motion, a semi‐generic wall motion based on patient‐specific motion, and patient‐specific wall motion based on medical images. We considered a LA geometry acquired from 4D computed tomography during AF, systematically performed convergence tests to assess the numerical accuracy of our solution strategy, and quantified the differences between the four approaches. The results revealed that wall motion had no discernible impact on LA cavity hemodynamics, nor on the markers that indicate thrombus formation. However, the flow patterns within the LAA deviated significantly in the rigid model, indicating that the assumption of rigid walls may lead to errors in the estimated risk factors. In contrast, the generic, semi‐generic, and patient‐specific cases were qualitatively similar. The results highlight the crucial role of wall motion on hemodynamics and predictors of thrombus formation, and also demonstrate the potential of using a generic motion model as a surrogate for the more complex patient‐specific motion. While the present study considered a single case, the employed CFD framework is entirely open‐source and designed for adaptability, allowing for integration of additional models and generic motions. [ABSTRACT FROM AUTHOR]

Most electromechanical cardiac models available in the literature are based on the Finite Element Method (FEM). Despite FEM's high accuracy, its performance is intrinsically dependent on the geometrical mesh quality onto which equations are discretized and solved. The construction and personalization of meshes representing the high complexity and inter-subject variability of anatomical structures such as the heart are often challenging and require tedious manual adjustments. Meshless methods are an interesting alternative to overcome meshing difficulties. The majority of meshless methods have been applied to simulate fluid dynamics problems but rarely in cardiac tissue electromechanics due to the lack of advanced numerical techniques to correct instabilities near the boundary conditions. In this paper, we propose a fully coupled model of cardiac electromechanics based on Total Lagrangian Smoothed Particle Hydrodynamics (TL-SPH). A zero-dimensional (0D) circulation model of hemodynamics was coupled to the 3D electromechanical model, linking ventricular volume changes along the cardiac cycle with pressures exerted on the endocardial surface of the heart. For calibration purposes, the proposed TL-SPH model was personalized to minimally invasive in vivo pressure measurements and in vivo magnetic resonance imaging (MRI) data of the left ventricle (LV) of a canine heart. The parameters of the electromechanics and hemodynamics models were estimated to fit LV volumetric and displacement data from MRI and available pressure signals in two phases: first, passive parameters based on diastolic data minimizing point-based differences between simulations and measurements; and, subsequently without any pressure information, active contraction and hemodynamics parameters based on volumetric and pressure data over the entire cardiac cycle. The TL-SPH model was able to reproduce pressure, volumetric and morphological indices from experimental measurements with good accuracy: end systolic / diastolic errors of 0.29 / 0.46 % and 1.82 / 4.18 % for maximal volume and pressure measurements, respectively. As this was achieved without the burden of mesh generation, the obtained results support the use of meshless methods as a valid alternative to standard FEM for cardiac electromechanical modeling, especially to assimilate point-based cardiac medical data. • The meshless method SPH allows the creation of a multi-physics cardiac model. • Automatic personalization of passive mechanics to clinical measurements. • Matching of imaging displacement fields. • Estimation of active mechanics. • Matching of pressure and volume measurements with good accuracy. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) studies of left atrial flows have reached a sophisticated level, for example, revealing plausible relationships between hemodynamics and stresses with atrial fibrillation. However, little focus has been on fundamental fluid modeling of LA flows. The purpose of this study was to investigate the spatiotemporal convergence, along with the differences between high‐ (HR) versus normal‐resolution/accuracy (NR) solution strategies, respectively. Rigid wall CFD simulations were conducted on 12 patient‐specific left atrial geometries obtained from computed tomography scans, utilizing a second‐order accurate and space/time‐centered solver. The convergence studies showed an average variability of around 30% and 55% for time averaged wall shear stress (WSS), oscillatory shear index (OSI), relative residence time (RRT), and endothelial cell activation potential (ECAP), even between intermediate spatial and temporal resolutions, in the left atrium (LA) and left atrial appendage (LAA), respectively. The comparison between HR and NR simulations showed good correlation in the LA for WSS, RRT, and ECAP (R2>.9), but not for OSI (R2=.63). However, there were poor correlations in the LAA especially for OSI, RRT, and ECAP (R2=.55,.63, and.61, respectively), except for WSS (R2=.81). The errors are comparable to differences previously reported with disease correlations. To robustly predict atrial hemodynamics and stresses, numerical resolutions of 10 M elements (i.e., Δx=∼.5 mm) and 10 k time‐steps per cycle seem necessary (i.e., one order of magnitude higher than normally used in both space and time). In conclusion, attention to fundamental numerical aspects is essential toward establishing a plausible, robust, and reliable model of LA flows. [ABSTRACT FROM AUTHOR]

Detection and delineation are key steps for retrieving and structuring information of the electrocardiogram (ECG), being thus crucial for numerous tasks in clinical practice. Digital signal processing (DSP) algorithms are often considered state-of-the-art for this purpose but require laborious rule readaptation for adapting to unseen morphologies. This work explores the adaptation of the the U-Net, a deep learning (DL) network employed for image segmentation, to electrocardiographic data. The model was trained using PhysioNet's QT database, a small dataset of 105 2-lead ambulatory recordings, while being independently tested for many architectural variations, comprising changes in the model's capacity (depth, width) and inference strategy (single- and multi-lead) in a fivefold cross-validation manner. This work features several regularization techniques to alleviate data scarcity, such as semi-supervised pre-training with low-quality data labels, performing ECG-based data augmentation and applying in-built model regularizers. The best performing configuration reached precisions of 90.12%, 99.14% and 98.25% and recalls of 98.73%, 99.94% and 99.88% for the P, QRS and T waves, respectively, on par with DSP-based approaches. Despite being a data-hungry technique trained on a small dataset, a U-Net based approach demonstrates to be a viable alternative for this task. [ABSTRACT FROM AUTHOR]

While the standard setting during radiofrequency catheter ablation (RFCA) consists of applying low power for long times, a new setting based on high power and short duration (HPSD) has recently been suggested as safer and more effective. Our aim was to compare the electrical and thermal performance of standard vs. HPSD settings, especially to assess the effect of the catheter orientation. A 3D computational model was built based on a coupled electric-thermal-flow problem. Standard (20 W–45 s and 30 W–30 s) and HPSD settings (70 W–7 s and 90 W–4 s) were compared. Since the model only included a cardiac tissue fragment, the power values were adjusted to 80% of the clinical values (15, 23, 53 and 69 W). Three catheter-tissue orientations were considered (90°, 45° and 0°). Thermal lesions were assessed by the Arrhenius equation. Safety was assessed by checking the occurrence of steam pops (100 °C in tissue) and thrombus formation (80 °C in blood). The computed thermal lesions were in close agreement with the experimental data in the literature, in particular with in vivo studies. HPSD created shallower and wider lesions than standard settings, especially with the catheter at 45°. Steam pops occurred earlier with HPSD, regardless of catheter orientation. HPSD seems to be more effective in cases that need shallow and extensive lesions, especially when the catheter is at 0° or at 45°, as used in pulmonary vein isolation. [ABSTRACT FROM AUTHOR]

Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind the medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions. • Interpretable deep learning helps to understand clinical and imaging attributes. • Latent space-based representations do not ensure control of data attributes. • Attribute-based methods help to explain different attributes in the latent space. • Attention maps explain attribute encoding in regularized latent space dimensions. [ABSTRACT FROM AUTHOR]

Objective: Three-dimensional (3D) time-resolved phase-contrast cardiac magnetic resonance (4D flow CMR) allows for unparalleled quantification of blood velocity. Despite established potential in aortic diseases, the analysis is time-consuming and requires expert knowledge, hindering clinical application. The present research aimed to develop and test a fully automatic machine learning-based pipeline for aortic 4D flow CMR analysis.Methods: Four hundred and four subjects were prospectively included. Ground-truth to train the algorithms was generated by experts. The cohort was divided into training (323 patients) and testing (81) sets and used to train and test a 3D nnU-Net for segmentation and a Deep Q-Network algorithm for landmark detection. In-plane (IRF) and through-plane (SFRR) rotational flow descriptors and axial and circumferential wall shear stress (WSS) were computed at ten planes covering the ascending aorta and arch.Results: Automatic aortic segmentation resulted in a median Dice score (DS) of 0.949 and average symmetric surface distance of 0.839 (0.632-1.071) mm, comparable with the state of the art. Aortic landmarks were located with a precision comparable with experts in the sinotubular junction and first and third supra-aortic vessels (p = 0.513, 0.592 and 0.905, respectively) but with lower precision in the pulmonary bifurcation (p = 0.028), resulting in precise localisation of analysis planes. Automatic flow assessment showed excellent (ICC > 0.9) agreement with manual quantification of SFRR and good-to-excellent agreement (ICC > 0.75) in the measurement of IRF and axial and circumferential WSS.Conclusion: Fully automatic analysis of complex aortic flow dynamics from 4D flow CMR is feasible. Its implementation could foster the clinical use of 4D flow CMR.Key Points: • 4D flow CMR allows for unparalleled aortic blood flow analysis but requires aortic segmentation and anatomical landmark identification, which are time-consuming, limiting 4D flow CMR widespread use. • A fully automatic machine learning pipeline for aortic 4D flow CMR analysis was trained with data of 323 patients and tested in 81 patients, ensuring a balanced distribution of aneurysm aetiologies. • Automatic assessment of complex flow characteristics such as rotational flow and wall shear stress showed good-to-excellent agreement with manual quantification. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is considered the most common human arrhythmia. In nonvalvular AF, around 99% of thrombi are formed in the left atrial appendage (LAA). Nevertheless, there is not a consensus in the community about the relevant factors to stratify the AF population according to thrombogenic risk.Objective: To demonstrate the need for combining left atrial morphological and haemodynamics indices to improve the thrombogenic risk assessment in nonvalvular AF patients.Methods: A cohort of 71 nonvalvular AF patients was analysed. Statistical analysis, regression models, and random forests were used to analyse the differences between morphological and haemodynamics parameters, extracted from computational simulations built on 3D rotational angiography images, between patients with and without transient ischemic attack (TIA) or cerebrovascular accident (CVA).Results: The analysis showed that models composed of both morphological and haemodynamic factors were better predictors of TIA/CVA compared with models based on either morphological or haemodynamic factors separately. Maximum ostium diameter, length of the centreline, blood flow velocity within the LAA, oscillatory shear index, and time average wall shear stress parameters were found to be key risk factors for TIA/CVA prediction. In addition, TIA/CVA patients presented more flow stagnation within the LAA.Conclusion: Thrombus formation in the LAA is the result of multiple factors. Analyses based only on morphological or haemodynamic parameters are not precise enough to predict such a phenomenon, as demonstrated in our results; a better patient stratification can be obtained by jointly analysing morphological and haemodynamic features. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new registration algorithm, called Temporal Diffeomorphic Free Form Deformation (TDFFD), and its application to motion and strain quantification from a sequence of 3D ultrasound (US) images. The originality of our approach resides in enforcing time consistency by representing the 4D velocity field as the sum of continuous spatiotemporal B-Spline kernels. The spatiotemporal displacement field is then recovered through forward Eulerian integration of the non-stationary velocity field. The strain tensor is computed locally using the spatial derivatives of the reconstructed displacement field. The energy functional considered in this paper weighs two terms: the image similarity and a regularization term. The image similarity metric is the sum of squared differences between the intensities of each frame and a reference one. Any frame in the sequence can be chosen as reference. The regularization term is based on the incompressibility of myocardial tissue. TDFFD was compared to pairwise 3D FFD and 3D+t FFD, both on displacement and velocity fields, on a set of synthetic 3D US images with different noise levels. TDFFD showed increased robustness to noise compared to these two state-of-the-art algorithms. TDFFD also proved to be more resistant to a reduced temporal resolution when decimating this synthetic sequence. Finally, this synthetic dataset was used to determine optimal settings of the TDFFD algorithm. Subsequently, TDFFD was applied to a database of cardiac 3D US images of the left ventricle acquired from 9 healthy volunteers and 13 patients treated by Cardiac Resynchronization Therapy (CRT). On healthy cases, uniform strain patterns were observed over all myocardial segments, as physiologically expected. On all CRT patients, the improvement in synchrony of regional longitudinal strain correlated with CRT clinical outcome as quantified by the reduction of end-systolic left ventricular volume at follow-up (6 and 12months), showing the potential of the proposed algorithm for the assessment of CRT. [Copyright &y& Elsevier]

• Statistical modelling of brain-heart interaction by including prior cardiac physiological knowledge. • Biophysical modelling constrained by a model of joint heart-brain variability. • The model identifies physiologically plausible mechanisms and significant differences associated to clinical conditions. • We present the first study on patient-specific modelling relating cardiac function and brain damage. • This study is based on the largest presented cohort of personalised subjects to date (3445 individuals). [Display omitted] Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our results demonstrate the impact of such external features in the cardiovascular model personalisation by learning more informative parameter-space constraints. Moreover, physiologically plausible mechanisms are captured through these personalised models as well as significant differences associated to specific clinical conditions. [ABSTRACT FROM AUTHOR]

Abstract: Spatial relations play an important role in recognition of structures embedded in a complex environment and for reasoning under imprecision. Several types of relationships can be modeled in a unified way using fuzzy mathematical morphology. Their combination benefits from the powerful framework of fuzzy set theory for fusion tasks and decision making. This paper presents several methods of fusion of information about spatial relationships and illustrates them on the example of model-based recognition of brain structures in 3D magnetic resonance imaging. [Copyright &y& Elsevier]

Clinical decision requires reasoning in the presence of imperfect data. DTs are a well-known decision support tool, owing to their interpretability, fundamental in safety-critical contexts such as medical diagnosis. However, learning DTs from uncertain data leads to poor generalization, and generating predictions for uncertain data hinders prediction accuracy. Several methods have suggested the potential of probabilistic decisions at the internal nodes in making DTs robust to uncertainty. Some approaches only employ probabilistic thresholds during evaluation. Others also consider the uncertainty in the learning phase, at the expense of increased computational complexity or reduced interpretability. The existing methods have not clarified the merit of a probabilistic approach in the distinct phases of DT learning, nor when the uncertainty is present in the training or the test data. We present a probabilistic DT approach that models measurement uncertainty as a noise distribution, independently realized: (1) when searching for the split thresholds, (2) when splitting the training instances, and (3) when generating predictions for unseen data. The soft training approaches (1, 2) achieved a regularizing effect, leading to significant reductions in DT size, while maintaining accuracy, for increased noise. Soft evaluation (3) showed no benefit in handling noise. [ABSTRACT FROM AUTHOR]

Introduction: Transthoracic echocardiography (TTE) is the gold standard in the assessment of diastolic dysfunction, however, measurement of diastolic markers such as the pulmonary vein (PV) inflow velocities is often challenging. 4D flow cardiac magnetic resonance imaging (CMR) enables quantification of velocities in any target volume without the constraints faced by TTE. We explored the use of 4D flow CMR in generating Doppler-like velocity spectrograms of mitral and PV inflow and comparing them to actual echo-based pulsed-wave (PW) Doppler velocity curves. Patients and Methods: A total of 6 HCM patients were included (P1-6). TTE was performed with a GE E95 system. Mitral and PV inflow PW Doppler was performed in the apical 4-chamber view by placing the sample volume at the level of the leaflet tips and at the inflow of the PV. The CMR data was acquired through a GE SIGNA Architect 3.0T scanner. The left atrium (LA), ventricle and outflow tract (LVOT) were segmented from a 4D flow phase contrast angiogram. The sample volume of PW Doppler was imitated using spheres of 4 mm radius placed in the same positions as in TTE. Velocities were projected in the direction typically captured by the echo transducer generating Doppler-like images. The duration of the pulmonary Ar and the mitral A wave was measured using both methods. Results: Figure 1 shows volumetric renderings of velocity at mid-systole. Compared to P1 and P2, P3-6 present an accelerated LVOT flow due to obstruction caused by systolic anterior motion, causing posterolateral mitral regurgitation disrupting LA hemodynamics. Figure 2 demonstrates the mitral and pulmonary inflow patterns obtained with TTE and CMR. Peak velocities and the duration of the A/ Ar waves were comparable when quantified using both modalities (Table 1). Ar wave duration could be quantified in all CMR studies, showing heterogeneity between the four PVs, whereas AR could be measured in only 3/6 of the echo studies. Conclusion: 4D CMR shows potential for blood flow assessment previously feasible only via TTE. CMR could provide a more reproducible assessment of PV inflow velocities, relevant in assessing diastolic function, and potentially reveal new insights about LA hemodynamics. [ABSTRACT FROM AUTHOR]

Background: Voltage mapping allows identifying the arrhythmogenic substrate during scar-related ventricular arrhythmia (VA) ablation procedures. Slow conducting channels (SCCs), defined by the presence of electrogram (EGM) signals with delayed components (EGM-DC), are responsible for sustaining VAs and constitute potential ablation targets. However, voltage mapping, as it is currently performed, is time-consuming, requiring a manual analysis of all EGMs to detect SCCs, and its accuracy is limited by electric far-field. We sought to evaluate an algorithm that automatically identifies EGM-DC, classifies mapping points, and creates new voltage maps, named "Slow Conducting Channel Maps" (SCC-Maps).Methods: Retrospective analysis of electroanatomic maps (EAM) from 20 patients (10 ischemic, 10 with arrhythmogenic right ventricular dysplasia/cardiomyopathy) was performed. EAM voltage maps were acquired during sinus rhythm and used for ablation. Preprocedural contrast-enhanced cardiac magnetic resonance (Ce-CMR) imaging was available for the ischemic population. Three mapping modalities were analysed: (i) EAM voltage maps using standard (EAM standard) or manual (EAM screening) thresholds for defining core and border zones; (ii) SCC-Maps derived from the use of the novel SCC-Mapping algorithm that automatically identify EGM-DCs measuring the voltage of the local component; and (iii) Ce-CMR maps (when available). The ability of each mapping modality in identifying SCCs and their agreement was evaluated.Results: SCC-Maps and EAM screening identified a greater number of SCC entrances than EAM standard (3.45 ± 1.61 and 2.95 ± 2.31, resp., vs. 1.05 ± 1.10; p < 0.01). SCC-Maps and EAM screening highly correlate with Ce-CMR maps in the ischemic population when compared to EAM standard (Lin's correlation = 0.628 and 0.679, resp., vs. 0.212, p < 0.01).Conclusion: The SCC-Mapping algorithm allows an operator-independent analysis of EGM signals showing better identification of the arrhythmogenic substrate characteristics when compared to standard voltage EAM. [ABSTRACT FROM AUTHOR]

Decision trees (DTs) are a widely used prediction tool, owing to their interpretability. Standard learning methods follow a locally optimal approach that trades off prediction performance for computational efficiency. Such methods can however be far from optimal, and it may pay off to spend more computational resources to increase performance. Monte Carlo tree search (MCTS) is an approach to approximate optimal choices in exponentially large search spaces. We propose a DT learning approach based on the Upper Confidence Bound applied to tree (UCT) algorithm, including procedures to expand and explore the space of DTs. To mitigate the computational cost of our method, we employ search pruning strategies that discard some branches of the search tree. The experiments show that proposed approach outperformed the C4.5 algorithm in 20 out of 31 datasets, with statistically significant improvements in the trade‐off between prediction performance and DT complexity. The approach improved locally optimal search for datasets with more than 1,000 instances, or for smaller datasets likely arising from complex distributions. This article is categorized under:Algorithmic Development > Hierarchies and TreesApplication Areas > Data Mining Software ToolsFundamental Concepts of Data and Knowledge > Data Concepts [ABSTRACT FROM AUTHOR]

Cardiac modeling has recently emerged as a promising tool to study pathophysiology mechanisms and to predict treatment outcomes for personalized clinical decision support. Nevertheless, achieving convergence under large deformation and defining a robust meshing for realistic heart geometries remain challenging, especially when maintaining the computational cost reasonable. Smoothed particle hydrodynamics (SPH) appears to be a promising alternative to the finite element method (FEM) since it removes the burden of mesh generation. A point cloud is used where each point (particle) contains all the physical properties that are updated throughout the simulation. SPH was evaluated for solid mechanics applications in the last decade but its capacity to address the challenge of simulating the mechanics of the heart has never been evaluated. In this paper, a total Lagrangian formulation of a corrected SPH was used to solve three solid mechanics problems designed to test important features that a cardiac mechanics solver should have. SPH results, in terms of ventricle displacements and strains, were compared to results obtained with 11 different FEM-based solvers, by using synthetic cardiac data from a benchmark study. In particular, passive dilation and active contraction were simulated in an ellipsoidal left ventricle with the exponential anisotropic constitutive law of Guccione following the direction of fibers. The proposed meshless method is able to reproduce the results of three benchmark problems for cardiac mechanics. Hyperelastic material with fiber orientation and high Poisson ratio allows wall thickening/thinning when large deformation is present. [ABSTRACT FROM AUTHOR]

This paper presents a design of a compact antenna applicator for a microwave colonoscopy system. Although colonoscopy is the most effective method for colorectal cancer detection, it suffers from important visualization restrictions that limit its performance. We recently reported that the contrast between healthy mucosa and cancer was 30%–100% for the relative permittivity and conductivity, respectively, at 8 GHz, and the complex permittivity increased proportionally to the degeneration rate of polyps (cancer precursors). The applicator is designed as a compact cylindrical array of eight antennas attached at the tip of a conventional colonoscope. The design presented here is a proof-of-concept applicator composed by one transmitting and one receiving cavity-backed U-shaped slot antenna elements fed by an L-shaped microstrip line. The antennas are low profile and present a high isolation at 8 GHz. The antenna performance is assessed with simulations and experimentally with a phantom composed by different liquids. [ABSTRACT FROM AUTHOR]

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions. [ABSTRACT FROM AUTHOR]

Purpose: Colorectal cancer is highly preventable by detecting and removing polyps, which are the precursors. Currently, the most accurate test is colonoscopy, but still misses 22% of polyps due to visualization limitations. In this paper, we preliminary assess the potential of microwave imaging and dielectric properties (e.g., complex permittivity) as a complementary method for detecting polyps and cancer tissue in the colon. The dielectric properties of biological tissues have been used in a wide variety of applications, including safety assessment of wireless technologies and design of medical diagnostic or therapeutic techniques (microwave imaging, hyperthermia, and ablation). The main purpose of this work is to measure the complex permittivity of different types of colon polyps, cancer, and normal mucosa in ex vivo human samples to study if the dielectric properties are appropriate for classification purposes. Methods: The complex permittivity of freshly excised healthy colon tissue, cancer, and histological samples of different types of polyps from 23 patients was characterized using an open‐ended coaxial probe between 0.5 and 20 GHz. The obtained measurements were classified into five tissue groups before applying a data reduction step with a frequency dispersive single‐pole Debye model. The classification was finally compared with pathological analysis of tissue samples, which is the gold standard. Results: The complex permittivity progressively increases as the tissue degenerates from normal to cancer. When comparing to the gold‐standard histological tissue analysis, the sensitivity and specificity of the proposed method is the following: 100% and 95% for cancer diagnosis; 91% and 62% for adenomas with high‐grade dysplasia; 100% and 61% for adenomas with low‐grade dysplasia; and 100% and 74% for hyperplastic polyps, respectively. In addition, complex permittivity measurements were independent of the lesion shape and size, which is also an interesting property comparing to current colonoscopy techniques. Conclusions: The contrast in complex permittivities between normal and abnormal colon tissues presented here for the first time demonstrate the potential of these measurements for tissue classification. It also opens the door to the development of a microwave endoscopic device to complement the outcomes of colonoscopy with functional tissue information. [ABSTRACT FROM AUTHOR]

Abstract: The left atrial appendage (LAA) is a complex and heterogeneous protruding structure of the left atrium (LA). In atrial fibrillation patients, it is the location where 90% of the thrombi are formed. However, the role of the LAA in thrombus formation is not fully known yet. The main goal of this work is to perform a sensitivity analysis to identify the most relevant LA and LAA morphological parameters in atrial blood flow dynamics. Simulations were run on synthetic ellipsoidal left atria models where different parameters were individually studied: pulmonary veins and mitral valve dimensions; LAA shape; and LA volume. Our computational analysis confirmed the relation between large LAA ostia, low blood flow velocities and thrombus formation. Additionally, we found that pulmonary vein configuration exerted a critical influence on LAA blood flow patterns. These findings contribute to a better understanding of the LAA and to support clinical decisions for atrial fibrillation patients. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular magnetic resonance (CMR) imaging has been used to visualise post-ablation atrial scar (PAAS), generally employing a three-dimensional (3D) late gadolinium enhancement (LGE) technique. However the reproducibility of PAAS imaging has not been determined. This cross-over study is the first to investigate the reproducibility of the technique, crucial for both future research design and clinical implementation. Methods: Forty subjects undergoing first time ablation for atrial fibrillation (AF) had detailed CMR assessment of PAAS. Following baseline pre-ablation scan, two scans (separated by 48 h) were performed at three months post-ablation. Each scan session included 3D LGE acquisition at 10, 20 and 30 min post administration of gadolinium-based contrast agent (GBCA). Subjects were allocated at second scan post-ablation to identical imaging parameters (‘Repro’, n = 10), 3 T scanner (‘3 T’, n = 10), half-slice thickness (‘Half-slice’, n = 10) or half GBCA dose (‘Half-gad’, n = 10). PAAS was compared to baseline scar and then reproducibility was assessed for two measures of thresholded scar (% left atrial (LA) occupied by PAAS (%LA PAAS) and Pulmonary Vein Encirclement (PVE)), and then four measures of non-thresholded scar (point-by-point assessment of PAAS, four normalisation methods). Thresholded measures of PAAS were evaluated against procedural outcome (AF recurrence). Results: A total of 271 3D acquisitions (out of maximum 280, 96.7%) were acquired. At 20 and 30 min, inter-scan reproducibility was good to excellent (coefficient of variation at 20 min and 30 min: %LA PAAS 0.41 and 0.20; PVE 0.13 and 0.04 respectively for ‘Repro’ group). Changes in imaging parameters, especially reduced GBCA dose, reduced interscan reproducibility, but for most measures remained good to excellent (ICC for %LA PAAS 0.454–0.825, PVE 0.618–0.809 at 30 min). For non-thresholded scar, highest reproducibility was observed using blood pool z-score normalisation technique: inter-scan ICC 0.759 (absolute agreement, ‘Repro’ group). There was no significant relationship between indices of PAAS and AF recurrence. Conclusion: PAAS imaging is a reproducible finding. Imaging should be performed at least 20 min post-GBCA injection, and a blood pool z-score should be considered for normalisation of signal intensities. The clinical implications of these findings remain to be established in the absence of a simple correlation with arrhythmia outcome. [ABSTRACT FROM AUTHOR]

In brain structural segmentation, multi-atlas strategies are increasingly being used over single-atlas strategies because of their ability to fit a wider anatomical variability. Patch-based label fusion (PBLF) is a type of such multi-atlas approaches that labels each target point as a weighted combination of neighboring atlas labels, where atlas points with higher local similarity to the target contribute more strongly to label fusion. PBLF can be potentially improved by increasing the discriminative capabilities of the local image similarity measurements. We propose a framework to compute patch embeddings using neural networks so as to increase discriminative abilities of similarity-based weighted voting in PBLF. As particular cases, our framework includes embeddings with different complexities, namely, a simple scaling, an affine transformation, and non-linear transformations. We compare our method with state-of-the-art alternatives in whole hippocampus and hippocampal subfields segmentation experiments using publicly available datasets. Results show that even the simplest versions of our method outperform standard PBLF, thus evidencing the benefits of discriminative learning. More complex transformation models tended to achieve better results than simpler ones, obtaining a considerable increase in average Dice score compared to standard PBLF. [ABSTRACT FROM AUTHOR]

Experimental studies on isolated cardiomyocytes from different animal species and human hearts have demonstrated that there are regional differences in the Ca2+ release, Ca2+ decay and sarcomere deformation. Local deformation heterogeneities can occur due to a combination of factors: regional/local differences in Ca2+ release and/or re-uptake, intra-cellular material properties, sarcomere proteins and distribution of the intracellular organelles. To investigate the possible causes of these heterogeneities, we developed a two-dimensional finite-element electromechanical model of a cardiomyocyte that takes into account the experimentally measured local deformation and cytosolic [Ca2+] to locally define the different variables of the constitutive equations describing the electro/mechanical behaviour of the cell. Then, the model was individualised to three different rat cardiac cells. The local [Ca2+] transients were used to define the [Ca2+]-dependent activation functions. The cell-specific local Young’s moduli were estimated by solving an inverse problem, minimizing the error between the measured and simulated local deformations along the longitudinal axis of the cell. We found that heterogeneities in the deformation during contraction were determined mainly by the local elasticity rather than the local amount of Ca2+, while in the relaxation phase deformation was mainly influenced by Ca2+ re-uptake. Our electromechanical model was able to successfully estimate the local elasticity along the longitudinal direction in three different cells. In conclusion, our proposed model seems to be a good approximation to assess the heterogeneous intracellular mechanical properties to help in the understanding of the underlying mechanisms of cardiomyocyte dysfunction. [ABSTRACT FROM AUTHOR]

Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high-quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1- and T2-weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio-temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772-2787, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Integration of electrical and structural information for scar characterization in the left ventricle (LV) is a crucial step to better guide radio-frequency ablation therapies, which are usually performed in complex ventricular tachycardia (VT) cases. This integration requires finding a common representation where to map the electrical information from the electro-anatomical map (EAM) surfaces and tissue viability information from delay-enhancement magnetic resonance images (DE-MRI). However, the development of a consistent integration method is still an open problem due to the lack of a proper evaluation framework to assess its accuracy. In this paper we present both: (i) an evaluation framework to assess the accuracy of EAM and imaging integration strategies with simulated EAM data and a set of global and local measures; and (ii) a new integration methodology based on a planar disk representation where the LV surface meshes are quasi-conformally mapped (QCM) by flattening, allowing for simultaneous visualization and joint analysis of the multi-modal data. The developed evaluation framework was applied to estimate the accuracy of the QCM-based integration strategy on a benchmark dataset of 128 synthetically generated ground-truth cases presenting different scar configurations and EAM characteristics. The obtained results demonstrate a significant reduction in global overlap errors (50–100%) with respect to state-of-the-art integration techniques, also better preserving the local topology of small structures such as conduction channels in scars. Data from seventeen VT patients were also used to study the feasibility of the QCM technique in a clinical setting, consistently outperforming the alternative integration techniques in the presence of sparse and noisy clinical data. The proposed evaluation framework has allowed a rigorous comparison of different EAM and imaging data integration strategies, providing useful information to better guide clinical practice in complex cardiac interventions. [ABSTRACT FROM AUTHOR]

The electrical activation of the heart is a complex physiological process that is essential for the understanding of several cardiac dysfunctions, such as ventricular tachycardia (VT). Nowadays, patient-specific activation times on ventricular chambers can be estimated from electro-anatomical maps, providing crucial information to clinicians for guiding cardiac radio-frequency ablation treatment. However, some relevant electrical pathways such as those of the Purkinje system are very difficult to interpret from these maps due to sparsity of data and the limited spatial resolution of the system. We present here a novel method to estimate these fast electrical pathways from the local activations maps (LATs) obtained from electro-anatomical maps. The location of Purkinje-myocardial junctions (PMJs) is estimated considering them as critical points of a distance map defined by the activation maps, and then minimal cost geodesic paths are computed on the ventricular surface between the detected junctions. Experiments to validate the proposed method have been carried out in simplified and realistic simulated data, showing good performance on recovering the main characteristics of simulated Purkinje networks (e.g. PMJs). A feasibility study with real cases of fascicular VT was also performed, showing promising results. [ABSTRACT FROM AUTHOR]

Electroanatomical mapping (EAM) systems are commonly used in clinical practice for guiding catheter ablation treatments of common arrhythmias. In focal tachycardias, the ablation target is defined by locating the earliest activation area determined by the joint analysis of electrogram (EGM) signals at different sites. However, this is currently a manual time-consuming and experience-dependent task performed during the intervention and thus prone to stress-related errors. In this paper, we present an automatic delineation strategy that combines electrocardiogram (ECG) information with the wavelet decomposition of the EGM signal envelope to identify the onset of each EGM signal for activation mapping. Fourteen electroanatomical maps corresponding to ten patients suffering from non-tolerated premature ventricular contraction (PVC) beats and admitted for ablation procedure were used for evaluation. We compared the results obtained automatically with two types of manual annotations: one during the intervention by an expert technician (on-procedure) and other after the intervention (off-procedure), free from time and procedural constraints, by two other technicians. The automatic annotations show a significant correlation (0.95, p $<$ 0.01) with the evaluation reference (off-procedure annotation sets combination) and has an error of 2.1 $\pm$ 10.9 ms, around the order of magnitude of the on-procedure annotations error ($-$ 2.6 $\pm$ 6.8 ms). The results suggest that the proposed methodology could be incorporated into EAM systems to considerably reduce processing time during ablation interventions. [ABSTRACT FROM PUBLISHER]

Intrauterine growth restriction (IUGR) due to placental insufficiency is associated with blood flow redistribution in order to maintain delivery of oxygenated blood to the brain. Given that, in the fetus the aortic isthmus (AoI) is a key arterial connection between the cerebral and placental circulations, quantifying AoI blood flow has been proposed to assess this brain sparing effect in clinical practice. While numerous clinical studies have studied this parameter, fundamental understanding of its determinant factors and its quantitative relation with other aspects of haemodynamic remodeling has been limited. Computational models of the cardiovascular circulation have been proposed for exactly this purpose since they allow both for studying the contributions from isolated parameters as well as estimating properties that cannot be directly assessed from clinical measurements. Therefore, a computational model of the fetal circulation was developed, including the key elements related to fetal blood redistribution and using measured cardiac outflow profiles to allow personalization. The model was first calibrated using patient-specific Doppler data from a healthy fetus. Next, in order to understand the contributions of the main parameters determining blood redistribution, AoI and middle cerebral artery (MCA) flow changes were studied by variation of cerebral and peripheral-placental resistances. Finally, to study how this affects an individual fetus, the model was fitted to three IUGR cases with different degrees of severity. In conclusion, the proposed computational model provides a good approximation to assess blood flow changes in the fetal circulation. The results support that while MCA flow is mainly determined by a fall in brain resistance, the AoI is influenced by a balance between increased peripheral-placental and decreased cerebral resistances. Personalizing the model allows for quantifying the balance between cerebral and peripheral-placental remodeling, thus providing potentially novel information to aid clinical follow up. [ABSTRACT FROM AUTHOR]

Understanding cardiac blood flow patterns has many applications in analysing haemodynamics and for the clinical assessment of heart function. In this study, numerical simulations of blood flow in a patient-specific anatomical model of the left ventricle (LV) and the aortic sinus are presented. The realistic 3D geometry of both LV and aortic sinus is extracted from the processing of magnetic resonance imaging (MRI). Furthermore, motion of inner walls of LV and aortic sinus is obtained from cine-MR image analysis and is used as a constraint to a numerical computational fluid dynamics (CFD) model based on the moving boundary approach. Arbitrary Lagrangian–Eulerian finite element method formulation is used for the numerical solution of the transient dynamic equations of the fluid domain. Simulation results include detailed flow characteristics such as velocity, pressure and wall shear stress for the whole domain. The aortic outflow is compared with data obtained by phase-contrast MRI. Good agreement was found between simulation results and these measurements. [ABSTRACT FROM AUTHOR]

In this paper, we present a modeling methodology to couple the cardiac conduction system to cardiac myocytes through a model of Purkinje-ventricular junctions to yield fast and realistic electrical activation of the ventricles. A patient-specific biventricular geometry is obtained from processing computed tomography scan data. A one-manifold implementation of the fast marching method based on Eikonal-type equations is used for modeling heart electrophysiology, which facilitates the multiscale 1-D–3-D coupling at very low computational costs. The method is illustrated in in-silico experiments where we analyze and compare alternative pacing strategies on the same patient-specific anatomy. We also show very good agreement between the results from the proposed approach and more detailed and comprehensive biophysical models for modeling cardiac electrophysiology. The effect of atrioventricular delay on the distribution of activation time in myocardium is studied with two experiments. Given the reasonable computational times and realistic activation sequences provided by our method, it can have an important clinical impact on the selection of optimal implantation sites of pacing leads or placement of ablation catheter’s tip in the context of cardiac rhythm management therapies. [ABSTRACT FROM AUTHOR]

Abstract: The VPH/Physiome Project is developing the model encoding standards CellML (cellml.org) and FieldML (fieldml.org) as well as web-accessible model repositories based on these standards (models.physiome.org). Freely available open source computational modelling software is also being developed to solve the partial differential equations described by the models and to visualise results. The OpenCMISS code (opencmiss.org), described here, has been developed by the authors over the last six years to replace the CMISS code that has supported a number of organ system Physiome projects. OpenCMISS is designed to encompass multiple sets of physical equations and to link subcellular and tissue-level biophysical processes into organ-level processes. In the Heart Physiome project, for example, the large deformation mechanics of the myocardial wall need to be coupled to both ventricular flow and embedded coronary flow, and the reaction–diffusion equations that govern the propagation of electrical waves through myocardial tissue need to be coupled with equations that describe the ion channel currents that flow through the cardiac cell membranes. In this paper we discuss the design principles and distributed memory architecture behind the OpenCMISS code. We also discuss the design of the interfaces that link the sets of physical equations across common boundaries (such as fluid-structure coupling), or between spatial fields over the same domain (such as coupled electromechanics), and the concepts behind CellML and FieldML that are embodied in the OpenCMISS data structures. We show how all of these provide a flexible infrastructure for combining models developed across the VPH/Physiome community. [Copyright &y& Elsevier]

Atrial fibrillation (AF) is nowadays the most common human arrhythmia and it is considered a marker of an increased risk of embolic stroke. It is known that 99% of AF-related thrombi are generated in the left atrial appendage (LAA), an anatomical structure located within the left atrium (LA). Left atrial appendage occlusion (LAAO) has become a good alternative for nonvalvular AF patients with contraindications to anticoagulants. However, there is a non-negligible number of device-related thrombus (DRT) events, created next to the device surface. In silico fluid simulations can be a powerful tool to better understand the relation between LA anatomy, haemodynamics, and the process of thrombus formation. Despite the increasing literature in LA fluid modelling, a consensus has not been reached yet in the community on the optimal modelling choices and boundary conditions for generating realistic simulations. In this line, we have performed a sensitivity analysis of several boundary conditions scenarios, varying inlet/outlet and LA wall movement configurations, using patient-specific imaging data of six LAAO patients (three of them with DRT at follow-up). Mesh and cardiac cycle convergence were also analysed. The boundary conditions scenario that better predicted DRT cases had echocardiography-based velocities at the mitral valve outlet, a generic pressure wave from an AF patient at the pulmonary vein inlets, and a dynamic mesh approach for LA wall deformation, emphasizing the need for patient-specific data for realistic simulations. The obtained promising results need to be further validated with larger cohorts, ideally with ground truth data, but they already offer unique insights on thrombogenic risk in the left atria. [ABSTRACT FROM AUTHOR]

• A grid-connected microgrid of a sport centre including a PV, a BESS and a heat pump. • Energy management strategies: 1) peak shaving and 2) pricing-based BESS operation. • Dynamic simulations in APROS & TRNSYS considering both thermal & electrical loads. • Pricing-based strategy yields to lower electricity cost for the examined time periods. Battery Energy Storage Systems (BESSs) are considered as one of the most promising technological options towards high self-sufficiency of building facilities and higher independency from the main grid through autonomous operation. At the same time, BESS are an essential component for a cost-optimized operation of grid-connected microgrids. In addition, the ever-increasing trend to replace conventional heating systems with electrically driven heat pumps, reveals a continuously emerging trend for electrification supported by electrical storage systems. In this study, a grid-connected microgrid of a sport centre facility located in Barcelona (Spain), which includes i) a solar PV (Photovoltaic) installation, ii) a BESS and iii) a heat pump (HP), is investigated through dynamic simulations, considering both thermal and electrical loads. Targeting the best compromise between self-sufficiency and reduction in the cost of electricity, two different energy management strategies are examined for the supervision and control of the grid-connected microgrid operation, taking into account the impact of the already operating HVAC (Heating Ventilation and Air Conditioning) systems along with the HP operation. In particular, the investigated energy management strategies are considered to be two of the most prominent ones and involve 1) peak shaving of the microgrid consumption with off-peak grid power and 2) pricing-based operation of the BESS (arbitrage), according to the main grid electricity price. Under this scope, an integrated thermal and electrical dynamic model of the sport centre energy system is developed in APROS software. The results are benchmarked against the corresponding ones from the standard model of TRNSYS. The impact of each management strategy is assessed in terms of the resulting power flows and the associated cost of electricity, for each operational mode. Based on the simulation results, the most advantageous energy management algorithm is determined for each case study, revealing that the integrated thermal-electrical dynamic modelling can be a useful tool for designing adaptive energy management algorithms. [ABSTRACT FROM AUTHOR]

• Cost-optimal design method for CCHP-assisted HP systems for varying climate scenarios. • Numerical transient model for demand and CCHP/HP system analysis. • Techno-economic analysis of Montjuic Castle, Barcelona, based on the model. • Small CCHP leads to more sustainable autonomy without unreasonable extra costs. • Climate change has a relatively small effect on comparative economic performance. An innovative modelling approach for the design of biomass-based, solar-assisted combined cooling, heating, and power (CCHP) and heat pump (HP) systems for various climate scenarios is proposed in this work. The modelling approach is comprised of three sub-models (a demand sub-model, a supply sub-model, an economic sub-model) allowing for cost-optimal sizing of the system components based on net present cost (NPC). Subsequently a transient optimization model has been developed, which computes the technical and economic performance for each component size and for each ambient temperature of the different climate scenarios. Additionally, the model provides data on energy efficiency, exergy efficiency, and CO 2 -emissions of a given CCHP/HP system. The model has then been employed in a case study in order to analyse the performance of a CCHP/HP system for a tourist, museum, and guest accommodation structure located at the Montjuïc castle in Barcelona, Spain. The results show that the smallest simulated biomass-based CCHP system using a 25 kW e syngas-fuelled engine would reduce lifetime costs by 7% compared to an only-HP system while operating with a high total energy efficiency of over 60%. The combined CCHP/HP system would operate with an exergy efficiency of 18%. However, larger CCHP systems cannot offset the much higher capital costs despite increasing electricity sales. The findings also reveal that for the high climate change scenario the overall project costs drop by up to 2.5%, however the effects have little impact on the optimal CCHP/HP system sizing. Even the smallest CCHP system would reduce emissions already by 75% with an increasing trend for larger systems. Although CCHP/HP systems would lead to lower NPC and emissions, the high investment costs and the complexity of the combined system remain considerable obstacles. [ABSTRACT FROM AUTHOR]

Rule‐based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule‐based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule‐based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum, and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modeling pipeline for creating patient‐specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule‐based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modeling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention. We have developed a rule‐based method (RBM) that includes specific fiber orientation in different cardiac regions such as the right ventricle endocardium, the interventricular septum, and the outflow tracts, following observations from histological data. This adapted RBM allows running in silico simulations aimed to model pathologies where these regions are relevant such as outflow tract ventricular arrhythmias. The resulting simulations with the proposed RBM showed a very good agreement with clinical parameters. [ABSTRACT FROM AUTHOR]