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• We introduce a coordinate system for the atria based on anatomical landmarks. • We construct the coordinates from solutions to Laplace's equation. • We demonstrate the mapping of both scalar and vector data between different atria. • The coordinate system was used for registration and 2D visualisation of multimodal data. • Patient specific meshes with atrial structures and fibre direction were constructed using just five landmark points. Integrating spatial information about atrial physiology and anatomy in a single patient from multimodal datasets, as well as generalizing these data across patients, requires a common coordinate system. In the atria, this is challenging due to the complexity and variability of the anatomy. We aimed to develop and validate a Universal Atrial Coordinate (UAC) system for the following applications: combination and assessment of multimodal data; comparison of spatial data across patients; 2D visualization; and construction of patient specific geometries to test mechanistic hypotheses. Left and right atrial LGE-MRI data were segmented and meshed. Two coordinates were calculated for each atrium by solving Laplace's equation, with boundary conditions assigned using five landmark points. The coordinate system was used to map spatial information between atrial meshes, including scalar fields measured using different mapping modalities, and atrial anatomic structures and fibre directions from a reference geometry. Average error in point transfer from a source mesh to a destination mesh and back again was less than 0.1 mm for the left atrium and 0.02 mm for the right atrium. Patient specific meshes were constructed using the coordinate system and phase singularity density maps from arrhythmia simulations were visualised in 2D. In conclusion, we have developed a universal atrial coordinate system allowing automatic registration of imaging and electroanatomic mapping data, 2D visualisation, and patient specific model creation. [ABSTRACT FROM AUTHOR]

Myocardial fiber orientation plays a critical role in the electrical activation and subsequent contraction of the heart. To increase the clinical potential of electrophysiological (EP) simulation for the study of cardiac phenomena and the planning of interventions, accurate personalization of the fibers is a necessary yet challenging task. Due to the difficulties associated with the in vivo imaging of cardiac fiber structure, researchers have developed alternative techniques to personalize fibers. Thus far, cardiac simulation was performed mainly based on rule-based fiber models. More recently, there has been a significant interest in data-driven and statistically derived fiber models. In particular, our predictive method in [1] allows us to estimate the unknown subject-specific fiber orientation based on the more easily available shape information. The aim of this work is to estimate the effect of using such statistical predictive models for the estimation of cardiac electrical activation times and patterns. To this end, we perform EP simulations based on a database of ten canine ex vivo diffusion tensor imaging (DTI) datasets that include normal and failing cases. To assess the strength of the fiber models under varying conditions, we consider both sinus rhythm and biventricular pacing simulations. The results show that 1) the statistically derived fibers improve the estimation of the local activation times by an average of 53.7% over traditional rule-based models, and that 2) the obtained electrical activations are consistently similar to those of the DTI-based fibers. [ABSTRACT FROM PUBLISHER]

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]

Being able to map a particular set of cardiac ventricles to a generic topologically equivalent representation has many applications, including facilitating comparison of different hearts, as well as mapping quantities and structures of interest between them. In this paper we describe Universal Ventricular Coordinates (UVC), which can be used to describe position within any biventricular heart. UVC comprise four unique coordinates that we have chosen to be intuitive, well defined, and relevant for physiological descriptions. We describe how to determine these coordinates for any volumetric mesh by illustrating how to properly assign boundary conditions and utilize solutions to Laplace’s equation. Using UVC, we transferred scalar, vector, and tensor data between four unstructured ventricular meshes from three different species. Performing the mappings was very fast, on the order of a few minutes, since mesh nodes were searched in a KD tree. Distance errors in mapping mesh nodes back and forth between meshes were less than the size of an element. Analytically derived fiber directions were also mapped across meshes and compared, showing  < 5° difference over most of the ventricles. The ability to transfer gradients was also demonstrated. Topologically variable structures, like papillary muscles, required further definition outside of the UVC framework. In conclusion, UVC can aid in transferring many types of data between different biventricular geometries. [ABSTRACT FROM AUTHOR]

The Myxococcales order consist of eleven families comprising30 genera, and are featured by the formation of the highest level of differential structure aggregations called fruiting bodies. These multicellular structures are essential for their resistance in ecosystems and is used in the primitive identification of these bacteria while their accurate taxonomic position is confirmed by the nucleotide sequence of 16SrRNA gene. Phenotypic classification of these structures is currently performed based on the stereomicroscopic observations that demand personal experience. The detailed phenotypic features of the genera with similar fruiting bodies are not readily distinctive by not particularly experienced researchers. The human examination of the fruiting bodies requires high skill and is error-prone. An image pattern analysis of schematic images of these structures conducted us to the construction of a database, which led to an extractable recognition of the unknown fruiting bodies. In this paper, Convolutional Neural Network (CNN) was considered as a baseline for recognition of fruiting bodies. In addition, to enhance the result the classifier, part of CNN is replaced with other classifiers. By employing the introduced model, all 30 genera of this order could be recognized based on stereomicroscopic images of the fruiting bodies at the genus level that not only does not urge us to amplify and sequence gene but also can be attained without preparation of microscopic slides of the vegetative cells or myxospores. The accuracy of 77.24% in recognition of genera and accuracy of 88.92% in recognition of suborders illustrate the applicability property of the proposed machine learning model. [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]

This paper presents a predictive framework for the statistical personalization of ventricular fibers. To this end, the relationship between subject-specific geometry of the left (LV) and right ventricles (RV) and fiber orientation is learned statistically from a training sample of ex vivo diffusion tensor imaging datasets. More specifically, the axes in the shape space which correlate most with the myocardial fiber orientations are extracted and used for prediction in new subjects. With this approach and unlike existing fiber models, inter-subject variability is taken into account to generate latent shape predictors that are statistically optimal to estimate fiber orientation at each individual myocardial location. The proposed predictive model was applied to the task of personalizing fibers in 10 canine subjects. The results indicate that the ventricular shapes are good predictors of fiber orientation, with an improvement of 11.4% in accuracy over the average fiber model. [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]

Purpose: To generate accurate and realistic models of coronary artery bifurcations before and after percutaneous coronary intervention (PCI), using information from two image modalities. Because bifurcations are regions where atherosclerotic plaque appears frequently and intervention is more challenging, generation of such realistic models could be of high value to predict the risk of restenosis or thrombosis after stent implantation, and to study geometrical and hemodynamical changes. Methods: Two image modalities have been employed to generate the bifurcation models: computer tomography angiography (CTA) to obtain the 3D trajectory of vessels, and 2D conventional coronary angiography (CCA) to obtain radius information of the vessel lumen, due to its better contrast and image resolution. In addition, CCA can be acquired right before and after the intervention in the operation room; therefore, the combination of CTA and CCA allows the generation of realistic preprocedure and postprocedure models of coronary bifurcations. The method proposed is semiautomatic, based on landmarks manually placed on both image modalities. Results: A comparative study of the models obtained with the proposed method with models manually obtained using only CTA, shows more reliable results when both modalities are used together. The authors show that using preprocedure CTA and postprocedure CCA, realistic postprocedure models can be obtained. Analysis carried out of the Murray's law in all patient bifurcations shows the geometric improvement of PCI in our models, better than using manual models from CTA alone. An experiment using a cardiac phantom also shows the feasibility of the proposed method. Conclusions: The authors have shown that fusion of CTA and CCA is feasible for realistic generation of coronary bifurcation models before and after PCI. The method proposed is efficient, and relies on minimal user interaction, and therefore is of high value to study geometric and hemodynamic changes of treated patients. [ABSTRACT FROM AUTHOR]