JOURNAL ARTICLES

Willems, R., Verberne, L., van der Sluis, O.,Verhoosel C.V., 2023. Echocardiogram-based ventricular isogeometric cardiac analysis using multi-patch fitted NURBS. Computer Methods in Applied Mechanics and Engineering.

 

Hatfaludi, C.A., Bunescu, D., Ciusdel, C.F., Serban, A., Bose, K., Oppel, M., Schroder, S., Seehase, C., Langer, H. F., Erdmann, J., Nording, H., Itu, L. M., 2023. Deep learning based detection of collateral circulation in coronary angiographies.
Annual IEEE Symposium on Computer-Based Medical Systems

Spanjaards, M., Borowski, F., Supp, L., Ubachs, R., Lavezzo, V., and van der Sluis, O., 2024. A fast in silico model for preoperative risk assessment of paravalvular leakage. Biomechanics and Modeling in Mechanobiology.

Hilhorst, P. L., Quicken, S., van de Vosse, F. N., & Huberts, W., 2023. Efficient sensitivity analysis for biomechanical models with correlated inputs. International journal for numerical methods in biomedical engineering.

Goubergrits L., Schafstedde M., Cesarovic N., Szengel A., Schmitt B., Wiegand M., Romberg J., Arndt A., Kuehne T., Brüning J., 2023. CT-based comparison of porcine, ovine, and human pulmonary arterial morphometry. Springer Nature

Verstraeten S., Hoeijmakers M., Tonino P., Brüning J., Capelli C., van de Vosse F., Huberts W., 2023. Generation of synthetic aortic valve stenosis geometries for in silico trials. International Journal of Numerical Methods in Biomedical Engineering.

Aydin B., Kiely E., Ohmann C., 2023. Feasibility assessment of using CDISC data standards for in silico medical device trialsJournal of the Society of Clinical Data Management.

Brüning J., Yevtushenko P., Schlief A., Jochum T., Van Gijzen L., Meine S., Romberg J., Kuehne T., Arndt A., Goubergrits L., 2023. In-silico enhanced animal study of pulmonary artery pressure sensors: assessing hemodynamics using computational fluid dynamics. Frontiers in Medical Technology.

Stoter, S.K.F., Divi, S.C., M., Harald van Brummelen, E., Larson, M.G., De Prenter, F.,  Verhoosel, C.V., 2023. Critical time-step size analysis and mass scaling by ghost-penalty for immersogeometric explicit dynamics. Computer Methods in Applied Mechanics and Engineering.

Lesage, R., Van Oudheusden, M., Schievano, S., Van Hoyweghen, I., Geris, L., Capelli, C., 2023. Mapping the use of computational modelling and simulation in clinics: A surveyFrontiers in Medical Technology.

Oldenburg, J., Borowski, F., Kaule, S., Schmitz, KP., Öner, A. & Stiehm, M., 2023. Analysis on the effects of hypo-attenuated leaflet thickening on the hemodynamics of transcatheter aortic valve prostheses by means of particle image velocimetry. tm – Technisches Messen.

Borowski, F., Kaule, S., Oldenburg, J., Schmitz, K.P., Öner, A., Stiehm, M., 2023. Analysis of thrombosis risk of commissural misaligned transcatheter aortic valve prostheses using particle image velocimetry. tm -Technisches Messen.

Borowski, F., Ott, R.,  Oldenburg, J., Kaule, S., Öner, A., Schmitz, K.P., Stiehm, M., 2022. Validation of a Fluid Structure Interaction Model for TAVR using Particle Image Velocimetry. Current Directions in Biomedical Engineering, 8(2), 512-515.

Oldenburg, J., Borowski, F., Schmitz, K. & Stiehm, M., 2022. Computation of flow through TAVI device by means of physics informed neural networks. Current Directions in Biomedical Engineering, 8(2), 741-744.

Oldenburg, J., Borowski, F., Öner, A., Schmitz, K.P. and Stiehm, M., 2022. Geometry aware physics informed neural network surrogate for solving Navier-Stokes equation (GAPINN). Advanced Modeling and Simulation in Engineering Sciences 9, 8.

Krüger, N., Meyer, A., Tautz, L., Hüllebrand, M., Wamala, I., Pullig, M., Kofler, M., Kempfert, J., Sündermann, S., Falk, V. and Hennemuth, A., 2022. Cascaded neural network-based CT image processing for aortic root analysisInternational Journal of Computer Assisted Radiology and Surgery17(3), pp.507-519.

Musuamba, F.T., Skottheim Rusten, I., Lesage, R., Russo, G., Bursi, R., Emili, L., Wangorsch, G., Manolis, E., Karlsson, K.E., Kulesza, A. and Courcelles, E., 2021. Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility. CPT: Pharmacometrics & Systems Pharmacology, 10(8), pp.804-825.

CONFERENCE PAPERS

Hatfaludi, C.A., Bunescu, D., Ciusdel, C.F., Serban, A., Bose, K., Oppel, M., Schroder, S., Seehase, C., Langer, H. F., Erdmann, J., Nording, H., Itu, L. M., 2023. Deep learning based detection of collateral circulation in coronary angiographies.
Annual IEEE Symposium on Computer-Based Medical Systems

Verstraeten, S., Hoeijmakers, M., van de Vosse, F., Huberts, W. Virtual cohort generation for in silico trials of transcatheter aortic valve implantation. 28th Annual Conference of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Brüning, J., Krüger, N., Yevtushenko, P., Goubergrits, L., Inter-species differences in pulmonary artery morphometry and hemodynamics. 28th Annual Conference of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Spanjaards, M., Ubachs, R., Lavezzo, V., Van der Sluis, O. Pre-operative risk assessment of paravalvular leakage using a computational tavi deployment model. 28th Annual Conference of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Moradi, H., van de Vosse, F., Huberts, W. Potential of using shell elements methods in fsi simulations of pulmonary arteries. 28th Annual Conference of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Hilhorst, P., Quicken, S., Van de Vosse, F., and Huberts, W. 2022. Sensitivity analysis of an one-dimensional pulse wave propagation model with correlated input. Virtual Physiological Human 2022 Conference.

Oldenburg, J., Borowski, F., Kaule, S., Öner, A., Schmitz, K.P., & Stiehm, M., 2022. Measurement of steady flow through a transcatheter aortic valve replacement by means of particle image velocimetry. German Association for Laser Anemometry (GALA) Annual Meeting.

Borowski, F., Oldenburg, J., Kaule, S., Schmitz, K.P., Öner, A., & Stiehm, M. 2022. Assessment of thrombogenic potential of prosthetic heart valves based on particle image velocimetry measurements. German Association for Laser Anemometry (GALA) Annual Meeting.

Lesage, R., Van Oudheusden, M., Contin, M., Schievano, S., and Capelli, C. 2022. Computer modelling and simulation in clinics: mapping usage and opinions for advancing in silico medicine. Virtual Physiological Human 2022 Conference.

Verstraeten, S., Suasso de Lima de Prado, D., Hoeijmakers, M., Van de Vosse, F., and Huberts, W. 2022. Non-parametric statistical shape modelling for in silico trials of TAVI. Virtual Physiological Human 2022 Conference.

Brüning, J., Yevtushenko, P., and Goubergrits, L. 2022. Validation of a synthetic cohort of aortic stenosis patients. Virtual Physiological Human 2022 Conference.

Moradi, H., Van de Vosse, F., and Huberts, W. 2022. Identification of the most influential factors on pulmonary artery hemodynamics using variance-based sensitivity analysis. Virtual Physiological Human 2022 Conference.

POSTERS

Hilhorst, P., 2022. Surrogate model based sensitivity analysis of a one dimensional arterial pulse wave propagation model with correlated input.Virtual Physiological Human 2022 Conference.

BOOK CHAPTERS

Verhoosel, C. V., Harald van Brummelen, E., Divi, S. C., & de Prenter, F. (2023). Scan-Based Immersed Isogeometric Flow Analysis. Frontiers in Computational Fluid-Structure Interaction and Flow Simulation: Research from Lead Investigators Under Forty-2023, 477-512.

Presentations

Ardnt, A. From engineering metrics to clinical endpoints. ESB2023 pre-course series, pre-course IV. From patient-based to in-silico trials: status quo and future perspectives.

Verde, P. Patient-based clinical trials. ESB2023 pre-course series, pre-course IV. From patient-based to in-silico trials: status quo and future perspectives.

Brüning, J. A statistical shape model of the porcine and human pulmonary artery for evaluation of medical devices. ESB2023 pre-course series, pre-course III. Virtual cohort generation, validation and application.

Verstraeten, S. Virtual cohort generation of aortic valve stenosis geometries. ESB2023 pre-course series, pre-course III. Virtual cohort generation, validation and application.

Huberts, W. Virtual cohort generation and validation: a multi-level methodology. ESB2023 pre-course series, pre-course III. Virtual cohort generation, validation and application.

Public Deliverables

D2.5 – Regulatory feedback report (1) (VPH, M19)

This deliverable reports the interactions of the SIMCor consortium with regulatory stakeholders at this point (M18) of the SIMCor project. Those interactions were facilitated via a dedicated SIMCor Regulatory Advisory Board (RAB) meeting or via a continuous liaison between regulators and the in-silico community at large via the VPH partner. The outcomes of the discussions are reported here, and conclusions are derived for future actions and directions.

D4.5 – SOPs for in-silico analysis of TAVI (IIB, M24)

This document was developed to provide two standard operating procedures (SOP) on flow field assessment of transcatheter aortic valve implants (TAVI) by means of numerical simulation applying fluid structure interaction (FSI) and particle image velocimetry measurements. The SOPs are designed for SIMCor partners as well as for the scientific community.

D6.3 – Uncertainty quantification for input data (CHA, M21)

Report of the uncertainty analysis of geometrical and functional model input data obtained by processing CT, MRI and sensor data.

D6.4 – Specification and quantification of subject-specific data-based boundary conditions (CHA, M24)

This document describes the assessment of boundary conditions required for in-silico assessment of device efficacy and safety for both project use cases, transcatheter aortic valve implantation (TAVI) and pulmonary artery pressure sensor (PAPS), using patient-specific information. These requirements include the patient- and subject-specific anatomy, i.e., the surface geometries of either the aorta or the pulmonary artery, as well as relevant functional boundary conditions, such as patient-specific volume flow rates in both the aorta and pulmonary artery, and pressure waveforms measured in the left ventricle and the aorta. The computational domains (i.e., anatomical geometries) and functional boundary conditions are used in the different models to assess the clinical endpoints selected for the two use-cases, as well as the validation of the respective models and virtual cohorts. All required information is either directly processed from medical image data and catheter- based pressure measurements or is calculated using hybrid approaches combining subject-specific measurements with models. All data is provided to all project partners via the virtual research environment (VRE). The VRE data allocation is also described in this document.

D6.5 – Specification and quantification of synthetic boundary conditions (CHA, M30)

This document provides specifications for the generation of synthetic boundary conditions. Synthetic boundary conditions are required in two instances. First, they can be used to describe virtual patients or datasets to be analysed via in-silico modelling. Second, they can be used to replace or impute missing information for in-silico assessment of real animal- or patient-specific cases. At first, a generic consideration regarding standardisation, relevant quality measures, and file formats is provided. Subsequently, the relevant synthetic boundary conditions for both project use cases, i.e., transcatheter aortic valve implantation (TAVI) and pulmonary artery pressure sensor (PAPS), are provided. The detailed descriptions of the use cases and data elements are used as dedicated examples for the more generic considerations. Here, the focus will lie on the generation of synthetic surface geometries, i.e., the anatomical boundary conditions, as the generation of functional boundary conditions, such as volume flow rates, was already addressed in D6.4 – Specification and quantification of subject-specific data-based boundary conditions (CHA, M24), both for patient-specific and synthetic cases.

D8.3 – Constitutive vessel model (TUG, M20)

Report on all relevant information to develop a simplified constitutive model to describe the material behaviour of the vessel wall, to be used in combination with the virtual device models for preliminary analyses requiring less computational effort. For this purpose, the constitutive framework of the Neo-Hookean and Fung-Demiray models is described and the implementation process in the commercial finite element software ANSYS Mechanical APDL and LS-DYNA is described in detail. Finally, two elementary numerical examples are given.

D8.4 – Validated constitutive models of the vessel wall (TUG, M26)

The deliverable reports the mechanical experiments and multi-photon microscopy imaging methods used to obtain the mechanical parameters and microstructural information of the ascending aorta and the pulmonary artery (main, left and right pulmonary artery) from healthy sheep, porcine and human tissue. In particular, the test set-up, the test protocol and the test samples are described for the biaxial extension test and the test protocol of the multi-photon microscopy method is detailed. Subsequently, the test results are presented. Lastly, based on the simplified vessel model (Deliverable 8.3 – Constitutive vessel model (TUG, M20)), an optimisation method is described to identify the constitutive parameters based on experimental data obtained from the literature and our laboratory.

D8.5 – Fast device deployment model (CHA, M24)

In this document, we report the results of T8.4 – Fast device deployment modelling. The goal of this task is to develop computationally efficient, physics-based models for simulation of device deployment and implement a prototype for the SIMCor use cases of PAPS and TAVI. The approach has to maintain an appropriate level of accuracy while simultaneously reducing the model complexity and numerical costs in comparison to the high-fidelity finite element approaches, such as the simulation model described in D8.6 – Report on 3D finite element simulation (PHI, M24). Only simplified parameterization as well as simple geometric anatomy and device representations are required. Such a simplified parameterization reduces preparation as well as simulation time and facilitates the automated processing of a large cohort. It enables interactive and automatic placement, e.g., regarding the orientation or angulation of devices inside a vessel.

D8.6 – Report on 3D finite element simulations (PHI, M24)

The aim of SIMCor is to establish in-silico methods for testing and validation of cardiovascular implantable devices, such as transcatheter aortic valve implantation (TAVI) prostheses as well as pulmonary artery pressure sensors (PAPS) and make those methods publicly available. In-silico simulations can be used to evaluate the safety, efficacy and applicability of medical devices and thus improve the quality of medical devices launched on the market. For this purpose, a numerical model has been developed in WP8 to perform device implantation and device effect simulations for TAVI. The model contains the patient specific (up until now still synthetic) aortic geometry, the TAVI stent geometry and calcification nodules on the valve leaflets. In an effort to reduce the computation time of the model, the TAVI stent was modelled using beam elements. The validity of this method is tested in a convergence study on a Representative Volume Element (RVE) of the CoreValve TAVI. Furthermore, a simple method to estimate the post-operative risk of paravalvular leakage is introduced. Note that the model described in this deliverable is also used to perform effect simulations used in deliverable D9.2 – Device specific models (BIO, M24) and D7.6 – Proof of principle of the complete virtual patient generator (TUE, M24).

 

D8.7 – Reduced-order model (PHI, M30)

In this deliverable we show the validity of a workflow for creating a Reduced-Order Model (ROM), trained by a limited number of high-fidelity deployment model evaluations to generate input for the fast-to-evaluate deployment model developed in D8.8 Fast-to-evaluate TAVI model based on multi-patch NURBS (TUE). The approximate shape of the deployed TAVI stent can be obtained as output of the fast-to-evaluate model. From this shape, patient-specific information like the paravalvular leakage (PVL) area for a selection of cross-sectional planes across the height of the aorta geometry can be obtained. This workflow can be used as a fast tool for pre- operative risk-assessment of the TAVI procedure. The results can be used to decide whether more accurate high- fidelity simulations are needed for a certain patient, thereby significantly reducing the costs of pre-operative risk assessment.

D8.8 – Fast-to-evaluate TAVI model based on multi-patch NURBS (TUE, M28)

In a transcatheter aortic valve implantation (TAVI) procedure, the native aortic valve is replaced by an artificial valve prosthesis. One of the objectives of SIMCor is to develop a simulation workflow to estimate TAVI treatment effects on large virtual patient cohorts. As part of this simulation workflow, this deliverable presents a fast-to-evaluate TAVI deployment model that provides quantitative insights into the positioning of the implanted device and its effect on post-implantation flow characteristics. The fast-to-evaluate model derives its computational performance from two key simulation technologies, viz.: (i) a multi-patch non-uniform rational b-splines (NURBS)-based anatomical model, and (ii) a time-dependent discrete contact mechanics model. The NURBS-based geometry description provides a parameterisation of the aorta and the aortic leaflets, from which a set of potential contact points is obtained. The time-dependent mechanical contact model, which solves for the balance of forces, determines the contact points between the TAVI device and the aorta at each time step.

The performance of the developed fast-to-evaluate model is assessed through a series of numerical experiments. A comparison with a high-fidelity finite element simulation (part of D8.6 – Report on 3D finite element simulation (PHI, M24)) is presented in this report, demonstrating the ability of the fast- to-evaluate model to predict the global characteristics of the TAVI procedure. Furthermore, a sensitivity analysis of the most important model parameters is considered to demonstrate the robustness of the fast-to-evaluate model. Furthermore, the developed model is used to study the impact of leaflet calcification on the TAVI procedure. Paravalvular leakage areas are compared between a case with and a case without leaflet calcification. The results demonstrate the capability of the fast-to-evaluate model to distinguish between such cases.

D9.1 – Constitutive vessel model (TUG, M27)

The deliverable reports all relevant information regarding the development of an enhanced constitutive model to describe the anisotropic material behaviour of the vessel wall, which will be used in combination with the virtual device models for preliminary analyses requiring a reduced computational effort. For this purpose, the constitutive framework of the Holzapfel-Gasser-Ogden model with non-symmetric fibre dispersion is described and subsequently the implementation process in the commercial finite element software ANSYS Mechanical APDL and LS-DYNA is detailed. Two elementary numerical examples are presented to demonstrate the characteristic material behaviour predicted by the model. Initially, the parameters for the Holzapfel-Gasser-Ogden model are derived from existing literature data and then refined using experimental findings from our laboratory. Furthermore, a Bayesian framework is applied in a representative case study to evaluate the uncertainty associated these material parameters.

D4.3 – SOPs for virtual cohorts generation and validation (TUE, M36)

This document was developed to provide a standard operating procedure (SOP) on the generation of virtual cohorts and their validation for use in in-silico trials. It aims to establish a systematic approach to building and validating virtual cohorts, for immediate use in device-effect simulation and thereby facilitate the in-silico trials. Considering the wide possibilities of ‘context(s) of use’ for virtual cohorts, we present a broad set of recommendations, building on the advances and the specific learnings from the two SIMCor use cases: transcatheter aortic valve implantation (TAVI) and pulmonary artery pressure sensors (PAPS). The SOP presents the cycle of development, validation, and application of virtual cohorts of pigs and humans, as applied to the above-mentioned use cases. Moreover, in theOutlook section, we sketch the broad perspectives on critical factors that need to be considered while attempting to generate virtual cohorts. Also, highlighted are the nuances related to their validation for different ‘context(s) of use’. This deliverable also includes two tangible applications of virtual cohorts, on the aortic valve and the pulmonary artery, for predicting patient-specific treatment outcomes like leakage post-implantation or device migration.

 

 

D4.6 – SOPs for validation of in silico models (BIO, IIB, CHA, M36)

Guidance of how to apply the ASME V&V 40 standard for in-silico model validation, with the examples of the clinical endpoints device perforation, device migration, thrombosis for the pulmonary artery pressure sensor as well as thrombosis, paravalvular leakage and durability for the transcatheter aortic valve.

D7.8 – Validated virtual cohorts for in-silico trials (TUE, CHA, M36)

In this report we describe the metadata of the virtual cohorts that will be made publicly available here. In addition, we will specify the validation steps that have been taken to end up with these cohorts (level of validation). Furthermore, we demonstrate the feasibility of using of our virtual cohorts for in-silico trials via simple effect simulations. At the end, we will indicate under which conditions/constraints our cohorts may be used for other applications/use cases (disclaimer).

D3.1 – System requirements (UTBV, M6)

Definition of technical requirements for the customisation and extension of the UTBV’s cloud-based infrastructure to integrate collected data and computational tools to facilitate the workflow of in silico clinical trials.

D4.1 – SOPs for data acquisition for in-silico models (UCL, M18)

Standard operating procedure (SOP) on clinical (secondary use) and preclinical in-vivo data acquisition in order to create virtual cohorts for in-silico modeling.

D7.5 – Uncertainty quantification and re-definition of input space (TUE, M18)

Demonstration of how the sensitivity analyses can be used to redefine the input space.

D7.4 – Sensitivity and uncertainty quantification toolbox (TUE, M16)

Proof-of-principle of the application of uncertainty quantification and sensitivity analysis to both aortic valve stenosis and heart failure patients.

D10.1 – In-silico trial impact assessment framework (ECRIN, M12)

Description of the conceptual framework for the in-silico trial impact assessment.

D4.4 – Guidelines for documentation (IIB, M12)

Recommendations on the formatting, organization, and content of reports describing in-silico modelling and results in the field of computational fluid dynamics (CFD) and finite element analysis (FEA) for medical device regulatory submissions.

D4.2 – Standard operating procedure for data processing for in-silico models (CHA, M12)

Standard operating procedure (SOP) on clinical data processing for creating virtual cohorts for in-silico models.

D7.3 – First version of the definition of the input space (TUE, M12)

First version of the definition of the input space that will be used to feed the virtual cohort generators.

D7.2 – Definition of model templates (TUE, M9)

Preliminary definition of model templates that will be used during virtual cohort generation of both aortic valve disease and heart failure patients.

D7.1 – Definition of model output (TUE, M6)

Preliminary definition of the physiological outputs to be used during virtual patient cohort generation of heart failure and aortic valve disease patients.

D1.4 – Self-assessment plan (LYN, M6)

Self-assessment plan of the project, including key performance indicators and procedures for completing the self-assessment for each work package.

D3.2 – Data management plan (LYN, M6)

Comprehensive analysis of the nature of data to be handled, generation, collection, de-identification and other processing, data flow and usage in the context of the project research activity and beyond, accessibility, interoperability, FAIR, long-term storage and backup, security measures adopted to prevent unauthorised access to personal data in the virtual research environment, and procedures for the inclusion of data and other resources in the European Open Science Cloud.

D1.5 – Quality assurance guidelines (LYN, M8)

Set of guidelines adopted for ensuring the highest quality in the execution of the project, including project management procedures, procedures for the preparation and quality control of project deliverables, reports and software.

D6.1 – Specification of data processing requirements (CHA, M4)

Requirements for medical image data processing for TAVI and PAPS use cases, including information to be extracted from medical images, relevant parameters and quality criteria.

D1.3 – Project handbook (LYN, M4)

Reference document for day-to-day project management, recapitulating project legal and ethical aspects, consortium partner composition and roles, management structure, procedures and tools, reporting guidelines, financial and budget issues, as well as guidelines for dissemination and communication of results.

D2.2 – Communication and dissemination strategy plan (LYN, M3)

Document illustrating the SIMCor communication and dissemination strategy plan, along with the relevant activities carried out in the first three months of project implementation.

D1.2 – Kick-off meeting report (LYN, M2)

Report summarising the project kick-off meeting, including general overview, work package presentations, working groups and discussions.

D2.1 – Project presentation (LYN, M1)

Description of project rationale, mission and objectives, consortium composition and role of partners, implementation and expected impacts in plain language, for communication and dissemination purposes.

Demos

VIDEOS

This 13′ collection of interview clips, released at the end of its 42-month activity, offers a comprehensive overview of its objectives, methods, results and foreseen impacts narrated by the team leader of each consortium partner.

This 3′ collection of interview clips represents the short version of ‘SIMCor: an overview’, providing a high-level overview of its objectives, methods, results and foreseen impacts narrated by the team leader of each consortium partner.

Poster pitch video | Virtual cohort generation for in silico trials of transcatheter aortic valve implantation. S. C. F. P. M. Verstraeten, M. J. M. M. Hoeijmakers, F. N. van de Vosse, W. Huberts. Presented at the 28th Annual Congress of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Press Releases

Horizon 2020 Research and Innovation Action kick-off: SIMCor (In-Silico testing and validation of Cardiovascular IMplantable devices)

Press release n.1/2021 of the SIMCor project, announcing project inception and conclusion of our kick-off meeting.

Leaflet

Leaflet

Leaflet illustrating project rationale, objectives and clinical focus.