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Introduction: Computational modeling-guided, personalized electrophysiology (EP) intervention for atrial fibrillation (AF) is an emerging paradigm of precision medicine. In the published models, advanced imaging and invasive mapping achieve personalization of cardiac anatomy. However, EP cellular personalization is less developed, and parameters are often assumed to be uniform across individual patients Hypothesis: Anatomical personalization alone is not sufficient to recapitulate the individual clinical features of AF in personalized models. Methods: For each of 57 patients (66±10 yr, 30 persistent) referred for catheter ablation of AF, we constructed a personalized 3-D model using pre-ablation CT, invasive mapping, and Courtemanche-Ramirez-Nattel atrial cell model. In the pre-ablation model, we performed virtual ramp pacing with cycle lengths at 200-120ms within the coronary sinus to induce AF. AF was defined as being inducible when it persists for >15s. Simulation was repeated for 5 different published sets of EP parameters of ionic currents. In total, >10k simulations were performed on an NVIDIA V100 GPU cluster, using the Lattice Boltzmann method with spatial resolution of 0.5mm and temporal resolution of 0.05ms, achieving a runtime of 24s per 1s of simulation ( Fig. A ). Results: While 100% AF inducibility was expected in the personalized pre-ablation models, AF inducibility varied between 27% and 96% depending on the set of ionic parameters used ( Fig. B ). The large variability of AF inducibility indicates that those models are highly sensitive to EP parameters, therefore anatomical personalization alone cannot adequately constrain the individual features of AF. Conclusions: The patient-specificity of the current paradigm of modeling-guided, personalized EP intervention is limited. There is an unmet clinical need for incorporation of personalized EP as well as anatomical parameters to achieve true precision medicine.

Background and Purpose— Therapeutic decision making for small unruptured intracranial aneurysms ( Methods— We analyzed 338 small unruptured aneurysms; 35 ruptured during the observation period, and 303 remained stable. Clinical, morphological, and hemodynamic parameters were considered. Computational fluid dynamics was used to calculate hemodynamic parameters based on computed tomography images of all aneurysms in their unruptured state. Differences between the ruptured and unruptured groups were tested by the Mann-Whitney U or Fisher exact tests. Multivariate logistic regression was applied to obtain a rupture risk model. Its predictive ability was investigated by receiver operating characteristic analysis. Results— The risk model revealed that rupture may be more likely to in younger patients (odds ratio [OR], 0.92 for each age increase of 1 year [95% CI, 0.88−0.96] P P =0.03), located at a bifurcation (OR, 5.45 [95% CI, 1.87−15.85] P =0.002), with a bleb (OR, 4.09 [95% CI, 1.42−11.79] P =0.009), larger length (OR, 1.91 for each increase of 1 mm [95% CI, 1.42−2.57] P P =0.01). The sensitivity, specificity, and area under the curve were 0.800, 0.752, and 0.826 (95% CI, 0.739−0.914) respectively. Conclusions— Younger age, presence of multiple aneurysms, location at a bifurcation, presence of a bleb, larger length, and lower pressure loss coefficient were identified as risk factors for rupture of small intracranial aneurysms. The risk model should be validated in further studies.

Electroanatomic mapping as routinely acquired in ablation therapy of ventricular tachycardia is the gold standard method to identify the arrhythmogenic substrate. To reduce the acquisition time and still provide maps with high spatial resolution, we propose a novel deep learning method based on graph convolutional neural networks to estimate the depolarization time in the myocardium, given sparse catheter data on the left ventricular endocardium, ECG, and magnetic resonance images. The training set consists of data produced by a computational model of cardiac electrophysiology on a large cohort of synthetically generated geometries of ischemic hearts. The predicted depolarization pattern has good agreement with activation times computed by the cardiac electrophysiology model in a validation set of five swine heart geometries with complex scar and border zone morphologies. The mean absolute error hereby measures 8 ms on the entire myocardium when providing 50% of the endocardial ground truth in over 500 computed depolarization patterns. Furthermore, when considering a complete animal data set with high density electroanatomic mapping data as reference, the neural network can accurately reproduce the endocardial depolarization pattern, even when a small percentage of measurements are provided as input features (mean absolute error of 7 ms with 50% of input samples). The results show that the proposed method, trained on synthetically generated data, may generalize to real data.

Coarctation of Aorta (CoA) is a congenital disease consisting of a narrowing that obstructs the systemic blood flow. This proof-of-concept study aimed to develop a framework for automatically and robustly personalizing aortic hemodynamic computations for the assessment of pre- and post-intervention CoA patients from 3D rotational angiography (3DRA) data. We propose a framework that combines hemodynamic modelling and machine learning (ML) based techniques, and rely on 3DRA data for non-invasive pressure computation in CoA patients. The key features of our framework are a parameter estimation method for calibrating inlet and outlet boundary conditions, and regional mechanical wall properties, to ensure that the computational results match the patient-specific measurements, and an improved ML based pressure drop model capable of predicting the instantaneous pressure drop for a wide range of flow conditions and anatomical CoA variations. We evaluated the framework by investigating 6 patient datasets, under pre- and post-operative setting, and, since all calibration procedures converged successfully, the proposed approach is deemed robust. We compared the peak-to-peak and the cycle-averaged pressure drop computed using the reduced-order hemodynamic model with the catheter based measurements, before and after virtual and actual stenting. The mean absolute error for the peak-to-peak pressure drop, which is the most relevant measure for clinical decision making, was 2.98 mmHg for the pre- and 2.11 mmHg for the post-operative setting. Moreover, the proposed method is computationally efficient: the average execution time was of only $$2.1 \pm 0.8$$ minutes on a standard hardware configuration. The use of 3DRA for hemodynamic modelling could allow for a complete hemodynamic assessment, as well as virtual interventions or surgeries and predictive modeling. However, before such an approach can be used routinely, significant advancements are required for automating the workflow.

This chapter presents a general overview on computational models of the heart. It focuses on mathematical modeling of the various physiological aspects involved in cardiac function, namely anatomy, electrophysiology, biomechanics, fluid dynamics and fluid structure interaction. Their implementation is described in the next chapter. The goal of this chapter is to give a brief introduction of each physiological system, and examples of mathematical models that have been developed so far by the community, along with their advantages and potential limitations in view of patient-specific simulations.

In recent years, computational fluid dynamics (CFD) has become a valuable tool for investigating hemodynamics in cerebral aneurysms. CFD provides flow-related quantities, which have been shown to have a potential impact on aneurysm growth and risk of rupture. However, the adoption of CFD tools in clinical settings is currently limited by the high computational cost and the engineering expertise required for employing these tools, e.g., for mesh generation, appropriate choice of spatial and temporal resolution, and of boundary conditions. Herein, we address these challenges by introducing a practical and robust methodology, focusing on computational performance and minimizing user interaction through automated parameter selection. We propose a fully automated pipeline that covers the steps from a patient-specific anatomical model to results, based on a fast, graphics processing unit- (GPU-) accelerated CFD solver and a parameter selection methodology. We use a reduced order model to compute the initial estimates of the spatial and temporal resolutions and an iterative approach that further adjusts the resolution during the simulation without user interaction. The pipeline and the solver are validated based on previously published results, and by comparing the results obtained for 20 cerebral aneurysm cases with those generated by a state-of-the-art commercial solver (Ansys CFX, Canonsburg PA). The automatically selected spatial and temporal resolutions lead to results which closely agree with the state-of-the-art, with an average relative difference of only 2%. Due to the GPU-based parallelization, simulations are computationally efficient, with a median computation time of 40 minutes per simulation.

This chapter describes a possible implementation of a comprehensive model of heart function. The presentation follows the same organization of the previous chapter. We first illustrate how the anatomical model can be build starting from medical images, and enriched with semantic information based on shape analysis. We then discuss efficient computational techniques for the electrophysiology model, based on Lattice–Boltzmann method for the approximation of the monodomain problem and on a graph-based solution of the Eikonal equation. The computation of surface electrical potential and ECG signals on the torso is also addressed, in particular based on the boundary element method. For the solution of the electromechanics problem we consider an efficient GPU-based implementation of the TLED method, discussing how to couple the biomechanics solver with the electrophysiology solver through the active force term, computed as a function of the electrical potential. Heart function depends critically on the interaction between heart tissue and blood: we dedicate specific attention to the implementation of boundary conditions for the electromechanics problem, including the coupling with models of the valves, pericardium bag and circulatory system. Finally, a section is devoted to computational models for hemodynamics, focusing in particular on techniques to solve the coupled fluid-structure interaction problem in the cardiac chambers.

3D+t Transesophageal Echocardiography (TEE) performs 4D scans of mitral valve (MV) morphology at frame rate providing real-time guidance for catheter-based interventions for MV repair and replacement. A key anatomical structure is the MV annulus, and live quantification of the dynamic annulus at acquisition rates 15 fps or higher have proven to be technically challenging. In this paper, we propose a bottom-up approach inspired by clinicians’ manual workflow for MV annulus modeling on 3D+t TEE images in real time. Specifically, we first detect annulus landmarks with clear 3D anatomical features via agents trained using Deep Reinforcement Learning. Leveraging the circular structure of the annulus, cross-annular planes are extracted and additional landmarks are then detected through 2D image-to-image networks on the 2D cutting planes. The complete 3D annulus is finally fitted through all detected landmarks using Splines. We validate the proposed approach on 795 3D+t TEE sequences with 1906 annotated frames, and achieve a speed 20 fps with a median accuracy 2.74 mm curve-to-curve error. Furthermore, device simulation is utilized to augment the training data that results in promising accuracy improvement on challenging echos with visible devices and warrants further investigation.

Purpose—Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. Methods—3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. Results—A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. Conclusions—Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters. Fil: Valen-Sendstad, Kristian. Simula Research Laboratory and Center for Cardiological Innovation; Noruega Fil: Bergersen, Aslak W.. University of Oslo; Noruega Fil: Shimogonya, Yuji. Nihon University; Japón Fil: Goubergrits, Leonid. Charite´ – Universita¨tsmedizin Berlin; Alemania Fil: Bruening, Jan. Charité – Universitätsmedizin Berlin; Alemania Fil: Pallares, Jordi. Universitat Rovira I Virgili; España Fil: Cito, Salvatore. Universitat Rovira I Virgili; España Fil: Piskin, Senol. University Of Texas At San Antonio.; Estados Unidos Fil: Pekkan, Kerem. Koc University; Turquía Fil: Geers, Arjan J.. Universitat Pompeu Fabra; España Fil: Larrabide, Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Rapaka, Saikiran. Siemens Medical Solutions USA Inc; Estados Unidos Fil: Mihalef, Viorel. Siemens Medical Solutions USA Inc; Estados Unidos Fil: Fu, Wenyu. Beijing Union University; China Fil: Qiao, Aike. Beijing University of Technology; China Fil: Jain, Kartik. Simula Research Laboratory and Center for Cardiological Innovation; Noruega. University of Siegen; Alemania. University of Zürich; Suiza Fil: Roller, Sabine. University of Siegen; Alemania Fil: Mardal, Kent-Andre. Simula Research Laboratory and Center for Cardiological Innovation; Noruega. University of Oslo; Noruega Fil: Kamakoti, Ramji. Dassault Systemes; Francia Fil: Spirka, Thomas. Simpleware Software Solutions; Reino Unido Fil: Ashton, Neil. University of Oxford; Reino Unido Fil: Revell, Alistair. University of Manchester; Reino Unido Fil: Aristokleous, Nicolas. University of Limerick; Irlanda Fil: Houston, J. Graeme. University of Dundee; Reino Unido Fil: Tsuji, Masanori. Mie Chuo Medical Center; Japón Fil: Ishida, Fujimaro. Mie Chuo Medical Center; Japón Fil: Menon, Prahlad G.. University of Pittsburgh; Estados Unidos Fil: Browne, Leonard D.. University of Limerick; Irlanda Fil: Broderick, Stephen. University of Limerick; Irlanda Fil: Shojima, Masaaki. University of Tokyo; Japón Fil: Koizumi, Satoshi. University of Tokyo; Japón Fil: Barbour, Michael. University of Washington; Estados Unidos Fil: Aliseda, Alberto. University of Washington; Estados Unidos Fil: Morales, Hernán G.. Medisys - Philips Research Paris; Francia Fil: Lefèvre, Thierry. Medisys - Philips Research Paris; Francia Fil: Hodis, Simona. Texas A&M University - Kingsville; Estados Unidos Fil: Al-Smadi, Yahia M.. Jordan University of Science and Technology; Jordania Fil: Tran, Justin S.. Stanford University; Estados Unidos Fil: Marsden, Alison L.. Stanford University; Estados Unidos Fil: Vaippummadhom, Sreeja. EinNel Technlogies; India Fil: Einstein, G. Albert. EinNel Technlogies; India Fil: Brown, Alistair G.. Siemens PLM Software; Estados Unidos Fil: Debus, Kristian. Siemens PLM Software; Estados Unidos Fil: Niizuma, Kuniyasu. Tohoku University; Japón Fil: Rashad, Sherif. Tohoku University; Japón Fil: Sugiyama, Shin-ichiro. Kohnan Hospital; Japón Fil: Owais Khan, M.. University of Toronto; Canadá Fil: Updegrove, Adam R.. University of California at Berkeley; Estados Unidos Fil: Shadden, Shawn C.. University of California at Berkeley; Estados Unidos Fil: Cornelissen, Bart M. W.. Academic Medical Center; Países Bajos Fil: Majoie, Charles B. L. M.. Academic Medical Center; Países Bajos Fil: Berg, Philipp. University of Magdeburg; Alemania Fil: Saalfield, Sylvia. University of Magdeburg; Alemania Fil: Kono, Kenichi. Wakayama Rosai Hospital; Japón Fil: Steinman, David A.. University of Toronto; Canadá

Simulating complex soft tissue deformations has been an intense research area in the fields of computer graphics or computational physiology for instance. A desired property is the ability to perform fast, if not real-time, simulations while being physically accurate. Numerical schemes have been explored to speed up finite element methods, like the Total Lagrangian Explicit Dynamics (TLED). However, real-time applications still come at the price of accuracy and fidelity. In this work, we explore the use of neural networks as function approximators to accelerate the time integration of TLED, while being generic enough to handle various geometries, motion and materials without having to retrain the neural network model. The method is evaluated on a set of experiment, showing promising accuracy at time steps up to 20 times larger than the “breaking” time step, as well as in a simple medical application. Such an approach could pave the way to very fast but accurate acceleration strategies for computational biomechanics.

Invasive peak-to-peak pressure gradients are the current clinical reference standard for assessing aortic coarctation. To obtain them, patients need to undergo arterial heart catheterization. Unless an intervention is performed, the procedure remains purely diagnostic, while the concomitant risks remain.To validate MRI-based pressure mapping against pressure drop derived from heart catheterization and to define minimal clinical requirements.Prospective clinical validation study.Twenty-seven coarctation patients with an indicated heart catheterization were enrolled at two clinical centers.1.5T including 4D velocity-encoded MRI and 3D anatomical imaging of the aorta.Pressure drop across the stenosis was calculated by pressure mapping based on the pressure Poisson equation. Calculated pressure drops were compared with catheter measured data. Spatial and temporal resolution were analyzed using in silico phantom-based data as well as in vivo measurements.Pressure drop was compared to peak-to-peak measurements. A two-sample paired mean equivalence test was used.In patients without imaging artifacts and a required spatial resolution ≥5 voxel/diameter, significant equivalence of pressure mapping compared to heart catheterization was found (17.5 ± 6.49 vs. 16.6 ± 6.53 mmHg, P 0.001).Pressure mapping provides equivalent accuracy to pressure drop obtained from heart catheterization in patients 1) without previous stenting and 2) with sufficient spatial image resolution (at least 5 voxels/diameter). In these patients the method can reliably be performed prior to the actual procedure, and thus allows safe noninvasive treatment planning based on MRI.2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:81-89.

We introduce a parameter estimation framework for automatically and robustly personalizing aortic haemodynamic computations from four-dimensional magnetic resonance imaging data. The framework is based on a reduced-order multiscale fluid–structure interaction blood flow model, and on two calibration procedures. First, Windkessel parameters of the outlet boundary conditions are personalized by solving a system of nonlinear equations. Second, the regional mechanical wall properties of the aorta are personalized by employing a nonlinear least-squares minimization method. The two calibration procedures are run sequentially and iteratively until both procedures have converged. The parameter estimation framework was successfully evaluated on 15 datasets from patients with aortic valve disease. On average, only 1.27 ± 0.96 and 7.07 ± 1.44 iterations were required to personalize the outlet boundary conditions and the regional mechanical wall properties, respectively. Overall, the computational model was in close agreement with the clinical measurements used as objectives (pressures, flow rates, cross-sectional areas), with a maximum error of less than 1%. Given its level of automation, robustness and the short execution time (6.2 ± 1.2 min on a standard hardware configuration), the framework is potentially well suited for a clinical setting.

Purpose: Coarctation of the aorta (CoA) is a congenital heart disease characterized by an abnormal narrowing of the proximal descending aorta. Severity of this pathology is quantified by the blood pressure drop (△P) across the stenotic coarctation lesion. In order to evaluate the physiological significance of the preoperative coarctation and to assess the postoperative results, the hemodynamic analysis is routinely performed by measuring the △P across the coarctation site via invasive cardiac catheterization. The focus of this work is to present an alternative, noninvasive measurement of blood pressure drop △P through the introduction of a fast, image-based workflow for personalized computational modeling of the CoA hemodynamics. Methods: The authors propose an end-to-end system comprised of shape and computational models, their personalization setup using MR imaging, and a fast, noninvasive method based on computational fluid dynamics (CFD) to estimate the pre- and postoperative hemodynamics for coarctation patients. A virtual treatment method is investigated to assess the predictive power of our approach. Results: Automatic thoracic aorta segmentation was applied on a population of 212 3D MR volumes, with mean symmetric point-to-mesh error of 3.00 ± 1.58 mm and average computation time of 8 s. Through quantitative evaluation of 6 CoA patients, good agreement between computed blood pressure drop and catheter measurements is shown: average differences are 2.38 ± 0.82 mm Hg (pre-), 1.10 ± 0.63 mm Hg (postoperative), and 4.99 ± 3.00 mm Hg (virtual stenting), respectively. Conclusions: The complete workflow is realized in a fast, mostly-automated system that is integrable in the clinical setting. To the best of our knowledge, this is the first time that three different settings (preoperative—severity assessment, poststenting—follow-up, and virtual stenting—treatment outcome prediction) of CoA are investigated on multiple subjects. We believe that in future—given wider clinical validation—our noninvasive in-silico method could replace invasive pressure catheterization for CoA.

Owing to its clinical importance, there has been a growing body of research on understanding the hemodynamics of cerebral aneurysms. Traditionally, this work has been performed using general-purpose, state-of-the-art commercial solvers. This has meant requiring engineering expertise for making appropriate choices on the geometric discretization, time-step selection, choice of boundary conditions etc. Recently, a CFD research prototype has been developed (Siemens Healthcare GmbH, Prototype — not for diagnostic use) for end-to-end analysis of aneurysm hemodynamics. This prototype enables anatomical model preparation, hemodynamic computations, advanced visualizations and quantitative analysis capabilities. In this study, we investigate the accuracy of the hemodynamic solver in the prototype against a commercially available CFD solver ANSYS CFX 16.0 (ANSYS Inc., Canonsburg, PA, www.ansys.com) retrospectively on a sample of twenty patient-derived aneurysm models, and show good agreement of hemodynamic parameters of interest.

In this chapter we introduce methodologies for modeling whole-body cardiovascular dynamics. Lumped parameter modeling techniques are employed to model both open-loop and closed-loop dynamics. The main constituents of the model are the pulmonary arterial and venous circulation, the systemic arterial and venous circulation, and the four chambers of the heart. A fully automated parameter estimation framework is introduced, which is based on two sequential steps: first, a series of parameters are computed directly, and, next, a fully automatic optimization-based calibration method is employed to iteratively estimate the values of the remaining parameters. A detailed sensitivity analysis has been performed for identifying the parameters which require calibration. Advanced objectives defined based on slopes and interval of times determined from the measured volume and pressure curves are formulated to improve the overall agreement between computed and measured quantities. Furthermore, methods for modeling subtle influences, e.g. from the KG diaphragm, and pathologic heart valves (stenosed, regurgitant) are introduced.

Solid voxelization represents the process of transforming a polygonal mesh into a voxel representation by associating each polygon of a mesh with the cells in the voxel grid. We introduce a novel approach for the voxelization of solid objects, designed for Graphics Processing Units (GPU). The method is based on a heuristic approach that computes an approximate distance field instead of using mesh surface normals or exact point-to-triangle distances. Two main steps are required: voxel marking and distance field computation. In the first step, each voxel is marked based on its location relative to the mesh (inside, outside of the domain or on its boundary), and, during the second step, a signed distance field is computed. Experiments focused on meshes encountered in medical imaging applications: a left ventricle and a coronary artery. The proposed method is found to be exceptionally robust as it is able to handle meshes with severe defects such as self intersections and holes. The GPU based implementation is on average 20 times faster than the multi-core CPU based implementation.

International audience; Purpose: We aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA). Methods: The RFA computation becomes subject-specific after several levels of personalization: geometrical and biophysical (hemodynamics, heat transfer and an extended cellular necrosis model). We present a comprehensive experimental setup combining multi-modal, pre-and post-operative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power. Results: To exploit this data set, an efficient processing pipeline is introduced, which copes with image noise, variable resolution and anisotropy. The validation study includes twelve ablations from five healthy pig livers: a mean point-to-mesh error between predicted and actual ablation extent of 5.3 ± 3.6 mm is achieved. Conclusion: This enables an end-to-end pre-clinical validation framework that considers the available data set.

In this paper we introduce a methodology for performing one-way Fluid-Structure interaction (FSI), i.e. where the motion of the wall boundaries is imposed. We use a Graphics Processing Unit (GPU) accelerated Lattice-Boltzmann Method (LBM) implementation and present an efficient workflow for embedding the moving geometry, given as a set of polygonal meshes, in the LBM computation. The proposed method is first validated in a synthetic experiment: a vessel which is periodically expanding and contracting. Next, the evaluation focuses on the 3D Peristaltic flow problem: a fluid flows inside a flexible tube, where a periodic wave-like deformation produces a fluid motion along the centerline of the tube. Different geometry configurations are used and results are compared against previously published solutions. The efficient approach leads to an average execution time of approx. one hour per computation, whereas 50% of it is required for the geometry update operations. Finally, we also analyse the effect of changing the Reynolds number on the flow streamlines: the flow regime is significantly affected by the Reynolds number.

Increasing interest is drawn on hemodynamic parameters for classifying the risk of rupture as well as treatment planning of cerebral aneurysms. A proposed method to obtain quantities such as wall shear stress, pressure, and blood flow velocity is to numerically simulate the blood flow using computational fluid dynamics (CFD) methods. For the validation of those calculated quantities, virtually generated angiograms, based on the CFD results, are increasingly used for a subsequent comparison with real, acquired angiograms. For the generation of virtual angiograms, several patient-specific parameters have to be incorporated to obtain virtual angiograms which match the acquired angiograms as best as possible. For this purpose, a workflow is presented and demonstrated involving multiple phantom and patient cases.

We present a new Eulerian-Lagrangian method for physics-based simulation of fluid flow, which includes automatic generation of sub-scale spray and bubbles. The Marker Level Set method is used to provide a simple geometric criterion for free marker generation. A filtering method, inspired from Weber number thresholding, further controls the free marker generation (in a physics-based manner). Two separate models are used, one for sub-scale droplets, the other for sub-scale bubbles. Droplets are evolved in a Newtonian manner, using a densityextension drag force field, while bubbles are evolved using a model based on Stokes’ Law. We show that our model for sub-scale droplet and bubble dynamics is simple to couple with a full (macro-scale) Navier-Stokes two-phase flow model and is quite powerful in its applications. Our animations include coarse grained multiphase features interacting with fine scale multiphase features.

In the present work we propose a simple and flexible fluid simulation method that enables us to perform one-way interactions of animated bodies with a coupled gas-liquid flow. This way we can animate flows in which not only the liquid but also the air is a performer. Our method allows the use of rigidly moving, articulated or deforming meshes. The paper shows how to do this practically, using a coupled level set and volume-of-fluid method. We also introduce to computer graphics a novel boundary condition, useful for open-field simulations. Animations of a swimmer in a pool, a hand scooping out water and a heart beating and spurting out either liquid or gas, showcase the strengths of our method.