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 survey. Frontiers 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 analysis. International Journal of Computer Assisted Radiology and Surgery, 17(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.

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.

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.

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.

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.

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.

Demo of the Virtual Research Environment (VRE). This video demonstrates the usability of the Virtual Cohort Generators that were embedded into the SIMCor Virtual Research Environment.

R-Statistical Environment embedded in the SIMCor Virtual Research Environment. The video shows how RStudio allows to run statistical evaluations in a standardised manner directly on the SIMCor Virtual Research Environment (VRE).

Clinically driven Virtual Cohort Generator for aortic valve geometries. This video demonstrates the generator for virtual geometries of the aortic rude, including the aortic valve leaflets based on clinically driven features.

Demo of TAVI deployment simulations using the virtual geometries and a high-fidelity model. This video demonstrates the feasibility of performing finite element TAVI deployment simulations in the virtual patient geometries generated using the Virtual Cohort Generator.

Demo of fast-to-evaluate surrogate models used for device implantation. This video illustrates a fast-to-evaluate model for TAVI device implantation based on isogeometric analysis.

SIMCor: in-Silico testing and validation of Cardiovascular IMplantable devices

Video teaser of the project

Pills of in-silico medicine Ep.1 | In-silico medicine

First episode of the series dedicated to in-silico medicine

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 illustrating project rationale, objectives and clinical focus.