Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models
Lead Research Organisation:
King's College London
Department Name: Imaging & Biomedical Engineering
Abstract
Clinical diagnosis is seldom definitive. Clinical data are noisy and sparse, and often support multiple diagnoses and potential therapies. To decide how best to treat a patient requires identifying the many possible outcomes for an individual and their corresponding probabilities. In this project we will apply the mathematics of uncertainty quantification, developed for automotive, geological and meteorological predictions, combined with biophysical models of individual patient physiology and pathophysiology to predict patient outcomes and their corresponding probabilities. This will demonstrate how patient specific computational models can be used to make prospective predictions to guide procedures and inform uncertain clinical decisions.
The use of uncertainty quantification and predictive patient specific models will be applied to patients with atrial fibrillation. Atrial fibrillation (AF) is the most common cardiac arrhythmia in the UK. In patients who do not respond to drug treatment, the pathological regions of the atria are removed or isolated through catheter ablation. However, up to 40% of patients with advanced (persistent) AF require further ablations to treat atrial tachycardia (pathological but regular activation) that develops after they have had an initial ablation to treat their AF. To reduce the number of additional procedures, this project will predict the probability that a patient will develop atrial tachycardia and the path that the atrial tachycardia will take, based on measurements recorded at the time of the initial persistent AF ablation procedure. If successful this approach would guide preventative ablations during the initial procedure to reduce the need for repeat procedures.
The use of uncertainty quantification and predictive patient specific models will be applied to patients with atrial fibrillation. Atrial fibrillation (AF) is the most common cardiac arrhythmia in the UK. In patients who do not respond to drug treatment, the pathological regions of the atria are removed or isolated through catheter ablation. However, up to 40% of patients with advanced (persistent) AF require further ablations to treat atrial tachycardia (pathological but regular activation) that develops after they have had an initial ablation to treat their AF. To reduce the number of additional procedures, this project will predict the probability that a patient will develop atrial tachycardia and the path that the atrial tachycardia will take, based on measurements recorded at the time of the initial persistent AF ablation procedure. If successful this approach would guide preventative ablations during the initial procedure to reduce the need for repeat procedures.
Planned Impact
The UK has a long history in developing cardiac electrophysiology models and this proposed research aims to continue this trajectory, moving computational models of cardiac electrophysiology into clinical applications. This project builds on the EPSRC investment at KCL in the Medical Engineering Centre, Medical Imaging Doctoral Training Centre and Fellowships and in Sheffield in the QUINTET project and POEMS network. Development of personalised models of the heart interacts with and integrates research across the EPSRC research portfolio. Primarily this work will support and develop the UK as an international leader in the clinical translation of cardiac modelling, contributing to the clinical technologies research area (RA). The process of creating quantitative and validated models of in-vivo cardiac tissue properties will contribute towards the biomaterials and tissue engineering RA, the need to better inform model parameters and improved understanding and quantification of cardiac physiology both motivate and exploit results from the medical imaging RA and the need for improved simulations times and robustness exploits, and drives, developments in the continuum mechanics RA and high performance computing. The adoption and development of statistical parameter inference and uncertainty quantification will feed into statistics and applied probability RA. This study will support the emergent industries using models as a healthcare service to guide procedures, improving patient outcomes and reducing costs, and for creating virtual patient cohorts for designing medical devices and improving cardio-toxicity screening. This project falls within the Clinical Technologies RA and in line with EPSRC guidance is focused heavily on translation to clinical impact and engaging with clinical end users. In light of this, the primary goal of this project is:
To move computational models of the heart from deterministic models that predict a single outcome for a patient to statistical tools that predict all of the possible outcomes and their likelihoods given the underlying uncertainty and sparsity in the available clinical data.
This transformative project will bring computational biophysical models from a position of a novel deterministic analysis tool to a predictive clinical application over 4 years. At the end of the project we will have:
1) Demonstrated the ability to create statistical patient specific biophysical models of the atria to predict the outcome and guide ablation procedures. This approach will be applicable to the ventricle and across other organ systems.
2) Applied supervoxel segmentation and uncertain marching cube algorithms to generate and visualise uncertain anatomical surfaces. These ideas can be extended to interpreting and visualising segmentation uncertainty in general.
3) Shown a novel role for uncertainty quantification within the clinic, giving mathematicians new avenues to apply and translate their research and deliver impact.
4) Predicted where to optimally ablate atrial fibrillation patients to minimize atrial tachycardia following an ablation to reduce the financial and social burden of repeat ablation procedures. This will provide clinical and basic science researchers with new understanding of atrial fibrillation and how this disease responds to treatment.
5) Generated clinical pilot data necessary to underpin the application for funding for the first biophysical patient specific model-guided atrial fibrillation ablation clinical trial.
6) Created a software platform that will enable other researchers to access patient specific model creation and statistical tools and demonstrate the value of these techniques to the healthcare technology industry.
7) Created a cohort of virtual patients for performing in-silico clinical trials. This will benefit clinical research by providing a test bed for new treatments and for device companies aiming to evaluate new technologies on virtual patients.
To move computational models of the heart from deterministic models that predict a single outcome for a patient to statistical tools that predict all of the possible outcomes and their likelihoods given the underlying uncertainty and sparsity in the available clinical data.
This transformative project will bring computational biophysical models from a position of a novel deterministic analysis tool to a predictive clinical application over 4 years. At the end of the project we will have:
1) Demonstrated the ability to create statistical patient specific biophysical models of the atria to predict the outcome and guide ablation procedures. This approach will be applicable to the ventricle and across other organ systems.
2) Applied supervoxel segmentation and uncertain marching cube algorithms to generate and visualise uncertain anatomical surfaces. These ideas can be extended to interpreting and visualising segmentation uncertainty in general.
3) Shown a novel role for uncertainty quantification within the clinic, giving mathematicians new avenues to apply and translate their research and deliver impact.
4) Predicted where to optimally ablate atrial fibrillation patients to minimize atrial tachycardia following an ablation to reduce the financial and social burden of repeat ablation procedures. This will provide clinical and basic science researchers with new understanding of atrial fibrillation and how this disease responds to treatment.
5) Generated clinical pilot data necessary to underpin the application for funding for the first biophysical patient specific model-guided atrial fibrillation ablation clinical trial.
6) Created a software platform that will enable other researchers to access patient specific model creation and statistical tools and demonstrate the value of these techniques to the healthcare technology industry.
7) Created a cohort of virtual patients for performing in-silico clinical trials. This will benefit clinical research by providing a test bed for new treatments and for device companies aiming to evaluate new technologies on virtual patients.
Organisations
Publications
Elliott MK
(2022)
Dispersion of repolarization increases with cardiac resynchronization therapy and is associated with left ventricular reverse remodeling.
in Journal of electrocardiology
Elliott MK
(2021)
Endocardial left ventricular pacing.
in Herz
Fassina D
(2023)
Assessing the arrhythmogenic risk of engineered heart tissue patches through in silico application on infarcted ventricle models.
in Computers in biology and medicine
Fastl TE
(2018)
Personalized computational modeling of left atrial geometry and transmural myofiber architecture.
in Medical image analysis
Feng L
(2019)
Analysis of a coupled fluid-structure interaction model of the left atrium and mitral valve.
in International journal for numerical methods in biomedical engineering
Fuchsberger J
(2022)
On the incorporation of obstacles in a fluid flow problem using a Navier-Stokes-Brinkman penalization approach
in Journal of Computational Science
Gould J
(2021)
Feasibility of intraprocedural integration of cardiac CT to guide left ventricular lead implantation for CRT upgrades.
in Journal of cardiovascular electrophysiology
Handa BS
(2018)
Analytical approaches for myocardial fibrillation signals.
in Computers in biology and medicine
| Description | We have developed computational tools for measuring error in images of the heart and in electrical measurements fo the heart that can now be used to constrain models of the heart |
| Exploitation Route | I have presented our results to Siemens, IBM and Abbott. I will also visit with medtronic later this year. All of these companies may be interested in these methods. |
| Sectors | Aerospace Defence and Marine Healthcare Manufacturing including Industrial Biotechology Pharmaceuticals and Medical Biotechnology |
| URL | https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/W000091/1 |
| Description | he EPSRC grant "Uncertainty Quantification in Prospective and Predictive Patient-Specific Cardiac Models" has had significant impacts on various fronts, notably in advancing research, fostering collaboration, and developing crucial software tools. Advancement of Digital Twin Program: The grant has played a pivotal role in supporting the development of a digital twin program at the Alan Turing Institute. Digital twins are virtual representations of physical systems that allow for real-time monitoring, analysis, and prediction. In the context of cardiac models, digital twins enable personalized simulations and predictive analytics, leading to better understanding and management of cardiac conditions. Isaac Newton Institute Workshops: Through the grant, workshops on cardiac uncertainty quantification have been organized at the Isaac Newton Institute. These workshops serve as platforms for researchers, academics, and industry professionals to exchange ideas, share insights, and collaborate on cutting-edge developments in the field. By bringing together experts in uncertainty quantification and cardiac modeling, these workshops facilitate interdisciplinary dialogue and catalyze innovation. Development of Emulation Software: One of the key impacts of the grant is the development of software for emulation. Emulation software allows researchers to efficiently approximate complex computational models, reducing computational costs while maintaining accuracy. In the context of cardiac models, emulation software enables rapid simulations and sensitivity analyses, facilitating the exploration of parameter spaces and the assessment of model uncertainties. Software for Interpolation on Arbitrary Manifolds: Additionally, the grant has led to the development of software for interpolation of fields on arbitrary manifolds. Manifold interpolation techniques are essential for analyzing and visualizing high-dimensional data, particularly in the context of patient-specific cardiac models where data may be irregularly sampled or distributed across multiple dimensions. This software enhances the capability to interpolate and analyze cardiac data, enabling researchers to gain deeper insights into cardiac dynamics and variability. Overall, the EPSRC grant has had a transformative impact on the field of cardiac modeling and uncertainty quantification, fostering collaboration, advancing research, and developing critical software tools that enhance our understanding of cardiac physiology and improve patient-specific healthcare outcomes. |
| First Year Of Impact | 2021 |
| Sector | Healthcare |
| Description | Atrial cardiac magnetic resonance imaging in patients with embolic stroke of unknown source without documented atrial fibrillation |
| Amount | £184,072 (GBP) |
| Organisation | British Heart Foundation (BHF) |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 10/2021 |
| Description | Development of a real time, patient-specific computational catheter ablation guidance tool utilising personalised structural and functional measurements |
| Amount | £354,064 (GBP) |
| Funding ID | 213342/Z/18/Z |
| Organisation | Wellcome Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 08/2019 |
| End | 09/2022 |
| Title | software for segmenting the atria |
| Description | We developed a software platform to facilitate segmentation of the atria from MRI |
| Type Of Material | Technology assay or reagent |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Impact | The code is now being used by a cor lab as part of a multi-centre prospective clinical trial |
| URL | http://cemrg.com/software/ |
| Title | A Publicly Available Virtual Cohort of Four-chamber Heart Meshes for Cardiac Electro-mechanics Simulations |
| Description | Motivation: Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. Dataset Description: We present the first database of four-chamber heart models suitable for electro-mechanical simulations. Our database consists of twenty-four four-chamber heart models generated from end-diastolic CT acquired from heart failure patients recruited for cardiac resynchronization therapy upgrade. We also provide a higher resolution version for each of the twenty-four meshes. We segmented end-diastolic CT. The segmentation was then upsampled and smoothed. The final multi-label segmentation was used to generate a tetrahedral mesh. The resulting meshes had an average edge length of 1.1mm. The elements of all the twenty-four meshes are labelled as follows: 1) Left ventricle myocardium 2) Right ventricle myocardium 3) Left atrium myocardium 4) Right atrium myocardium 5) Aorta wall 6) Pulmonary artery wall 7) Left atrium appendage ring 8) Left superior pulmonary vein ring 9) Left inferior pulmonary vein ring 10) Right inferior pulmonary vein ring 11) Right superior pulmonary vein ring 12) Superior vena cava ring 13) Inferior vena cava ring 14) Mitral valve plane 15) Tricuspid valve plane 16) Aortic valve plane 17) Pulmonary valve plane 18) Left atrial appendage valve plane 19) Left superior pulmonary vein valve plane 20) Left inferior pulmonary vein valve plane 21) Right inferior pulmonary vein valve plane 22) Right superior pulmonary vein valve plane 23) Superior vena cava valve plane 24) Inferior vena cava valve plane. Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80° to -60°, respectively. We defined a system of universal ventricular coordinates on the meshes, see Figure 1B: an apico-basal coordinate varying continuously from 0 at the apex to 1 at the base; a transmural coordinate varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate varying continuously from - p at the left ventricular free wall, 0 at the septum and then back to + p at the left ventricular free wall; intra-ventricular coordinate defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -100. We also refined each mesh from 1.1mm resolution down to 0.39mm resolution. Each refined mesh has tags defined on its elements (same numbering as described above) and ventricular fibres. Database format: We provide a zipped folder for each mesh. Each folder contains the coarse and the finer versions of the same mesh. All twenty-four 1mm-meshes are supplied in case format, readable with paraview. All binary files containing the meshes data (ens and geo formats) are provided within the zipped folder. Points coordinates are given in mm. Element tags are assigned to the elements of the mesh as well as fibres and sheet directions. Fibres and sheet directions are assigned to the ventricles according to a rule-based method, while non-ventricular elements are assigned with default vectors [1; 0; 0] and [0; 1; 0]. UVCs are assigned to the nodes of the meshes. We also provide the location of the cardiac resynchronisation therapy right-ventricular electrode used to initiate ventricular excitation. This is given as a label on the nodes called electrode endo rv, which is 1 at the stimulated nodes. Finer meshes are provided in vtk format, also readable in paraview. For these meshes, we provide element tags, fibres and sheet directions on the ventricles, all in the same file. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3890034 |
| Title | A Publicly Available Virtual Cohort of Four-chamber Heart Meshes for Cardiac Electro-mechanics Simulations |
| Description | Motivation: Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. Dataset Description: We present the first database of four-chamber heart models suitable for electro-mechanical simulations. Our database consists of twenty-four four-chamber heart models generated from end-diastolic CT acquired from heart failure patients recruited for cardiac resynchronization therapy upgrade. We also provide a higher resolution version for each of the twenty-four meshes. We segmented end-diastolic CT. The segmentation was then upsampled and smoothed. The final multi-label segmentation was used to generate a tetrahedral mesh. The resulting meshes had an average edge length of 1.1mm. The elements of all the twenty-four meshes are labelled as follows: 1) Left ventricle myocardium 2) Right ventricle myocardium 3) Left atrium myocardium 4) Right atrium myocardium 5) Aorta wall 6) Pulmonary artery wall 7) Left atrium appendage ring 8) Left superior pulmonary vein ring 9) Left inferior pulmonary vein ring 10) Right inferior pulmonary vein ring 11) Right superior pulmonary vein ring 12) Superior vena cava ring 13) Inferior vena cava ring 14) Mitral valve plane 15) Tricuspid valve plane 16) Aortic valve plane 17) Pulmonary valve plane 18) Left atrial appendage valve plane 19) Left superior pulmonary vein valve plane 20) Left inferior pulmonary vein valve plane 21) Right inferior pulmonary vein valve plane 22) Right superior pulmonary vein valve plane 23) Superior vena cava valve plane 24) Inferior vena cava valve plane. Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80° to -60°, respectively. We defined a system of universal ventricular coordinates on the meshes, see Figure 1B: an apico-basal coordinate varying continuously from 0 at the apex to 1 at the base; a transmural coordinate varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate varying continuously from - p at the left ventricular free wall, 0 at the septum and then back to + p at the left ventricular free wall; intra-ventricular coordinate defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -100. We also refined each mesh from 1.1mm resolution down to 0.39mm resolution. Each refined mesh has tags defined on its elements (same numbering as described above) and ventricular fibres. Database format: We provide a zipped folder for each mesh. Each folder contains the coarse and the finer versions of the same mesh. All twenty-four 1mm-meshes are supplied in case format, readable with paraview. All binary files containing the meshes data (ens and geo formats) are provided within the zipped folder. Points coordinates are given in mm. Element tags are assigned to the elements of the mesh as well as fibres and sheet directions. Fibres and sheet directions are assigned to the ventricles according to a rule-based method, while non-ventricular elements are assigned with default vectors [1; 0; 0] and [0; 1; 0]. UVCs are assigned to the nodes of the meshes. We also provide the location of the cardiac resynchronisation therapy right-ventricular electrode used to initiate ventricular excitation. This is given as a label on the nodes called electrode endo rv, which is 1 at the stimulated nodes. Finer meshes are provided in vtk format, also readable in paraview. For these meshes, we provide element tags, fibres and sheet directions on the ventricles, all in the same file. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3890033 |
| Title | Anatomically Detailed Human Atrial FE Meshes |
| Description | The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. We include 3 patient-specific anatomical FE meshes with rule-based myofiber directions of each of the anatomies included in our study ("The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium", BMMB, 2020, https://pubmed.ncbi.nlm.nih.gov/31802292/).
Additionally we include - a model with Gaussian noise added (mean 0 um , standard deviation 100 um) to the initial geometry of patient case 3 and subsequently smoothed using ParaView; and - a mesh with a constant wall thickness of 0.5 mm generated based on the endocardial surface of patient case 3. The meshes are given in VTK file format (.vtu) and in the binary format used for the Cardiac Arrhythmia Research Package simulator, see https://carpentry.medunigraz.at/carputils/index.html and https://opencarp.org. Here, for each of the geometries, we include a list of nodal coordinates (.bpts file), a list of triangular elements (.belem file), fiber fields (.blon file), surface files (*.surf files), and surface points (*.surf.vtx files). Surface files include the endocardium (laendo.surf), the epicardium (laepi.surf), the mitral valve ring (mitralvv.surf), the pulmonary outlet rings (pulvring.surf) and lids (lid*.vtx) to close the five in- and outlets of the LA. Using the open source mesh utiliy "MeshTool" (https://bitbucket.org/aneic/meshtool/src/master/README.md) meshes can be manipulated or converted to VTK or EnSight file formats. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3843215 |
| Title | Anatomically Detailed Human Atrial FE Meshes |
| Description | The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. We include 3 patient-specific anatomical FE meshes with rule-based myofiber directions of each of the anatomies included in our study ("The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium", BMMB, 2020, https://pubmed.ncbi.nlm.nih.gov/31802292/).
Additionally we include - a model with Gaussian noise added (mean 0 um , standard deviation 100 um) to the initial geometry of patient case 3 and subsequently smoothed using ParaView; and - a mesh with a constant wall thickness of 0.5 mm generated based on the endocardial surface of patient case 3. The meshes are given in VTK file format (.vtu) and in the binary format used for the Cardiac Arrhythmia Research Package simulator, see https://carpentry.medunigraz.at/carputils/index.html and https://opencarp.org. Here, for each of the geometries, we include a list of nodal coordinates (.bpts file), a list of triangular elements (.belem file), fiber fields (.blon file), surface files (*.surf files), and surface points (*.surf.vtx files). Surface files include the endocardium (laendo.surf), the epicardium (laepi.surf), the mitral valve ring (mitralvv.surf), the pulmonary outlet rings (pulvring.surf) and lids (lid*.vtx) to close the five in- and outlets of the LA. Using the open source mesh utiliy "MeshTool" (https://bitbucket.org/aneic/meshtool/src/master/README.md) meshes can be manipulated or converted to VTK or EnSight file formats. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3843659 |
| Title | Anatomically Detailed Human Atrial FE Meshes |
| Description | The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. We include 3 patient-specific anatomical FE meshes with rule-based myofiber directions of each of the anatomies included in our study (The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium, BMMB, 2020, https://pubmed.ncbi.nlm.nih.gov/31802292/).
Additionally we include - a noised model with Gaussian noise added (mean 0 um , standard deviation 100 um ) to the initial geometry of patient case 3 and subsequently smoothed using ParaView; and - a mesh with a constant wall thickness of 0.5 mm generated based on the endocardial surface of patient case 3. The meshes are in the binary format for the Cardiac Arrhythmia Research Package simulator, see https://carpentry.medunigraz.at/carputils/index.html and https://opencarp.org. For each of the geometries, we include a list of nodal coordinates (.bpts file), a list of triangular elements (.belem file), fiber fields (.blon file), surface files (*.surf files), and surface points (*.surf.vtx files). Using the open source mesh manipulation utiliy "MeshTool" (https://bitbucket.org/aneic/meshtool/src/master/README.md) meshes can be converted to VTK or EnSight file formats. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3843216 |
| Title | Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets |
| Description | This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology. Every folder contains two csv files: - parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis - outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample In ventricular_cell_model, there are four folders: - ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features - contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0). - contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1). - contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic. The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient. The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model. The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation. The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model. Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function. The details about the model can be found in the original publication. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/7405334 |
| Title | Constructing a Human Atrial Fibre Atlas, Roney et al. |
| Description | Background: Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. Dataset Description: We include endocardial and epicardial left and right atrial surfaces for each of the 7 anatomies included in our study (Constructing a Human Atrial Fibre Atlas, ABME, 2020), together with their fibre fields. We also include the average fibre field for each of the atrial surfaces displayed on anatomy number 6 (named *_A). For each of the surfaces, we also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA (IVC-SVC coordinate for the RA). More details on the coordinate construction are given in our manuscript and https://www.ncbi.nlm.nih.gov/pubmed/31026761. These coordinates can be used for registering datasets. These meshes are in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the fibre field defined on the elements. We have also included mesh files for the Cardiac Arrhythmia Research Package simulator. These are a list of nodal coordinates (.pts file), a list of triangular elements (.elem file), and a fibre file (.lon). More details on this file format and the carpentry simulator are available at: https://carpentry.medunigraz.at/carputils/index.html. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3764917 |
| Title | Constructing a Human Atrial Fibre Atlas, Roney et al. |
| Description | Background: Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. Dataset Description: We include endocardial and epicardial left and right atrial surfaces for each of the 7 anatomies included in our study (Constructing a Human Atrial Fibre Atlas, ABME, 2020), together with their fibre fields. We also include the average fibre field for each of the atrial surfaces displayed on anatomy number 6 (named *_A). For each of the surfaces, we also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA (IVC-SVC coordinate for the RA). More details on the coordinate construction are given in our manuscript and https://www.ncbi.nlm.nih.gov/pubmed/31026761. These coordinates can be used for registering datasets. These meshes are in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the fibre field defined on the elements. We have also included mesh files for the Cardiac Arrhythmia Research Package simulator. These are a list of nodal coordinates (.pts file), a list of triangular elements (.elem file), and a fibre file (.lon). More details on this file format and the carpentry simulator are available at: https://carpentry.medunigraz.at/carputils/index.html. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/3764916 |
| Title | Model Reproducibility Study on Left Atrial Fibres |
| Description | This dataset contains 100 models of the left atrium. Models come from 50 distinct patients, divided amongst 5 users to assess for inter- and intra-operator variability. The split used was 30 pairs (60 models) for inter-operator variability and 20 pairs (40 models) for intra operator variability. Models were created with a specific version of the software CemrgApp (cemrgapp.com), in which users processed a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Two types of simulations were run on each of the 100 processed cases: baseline pacing to calculate local activation time (LAT) maps and atrial fibrillation simulations for which phase singularity (PS) maps were calculated. The openCARP simulator (Plank et al., 2021) was used to run the simulations, using the Courtemance human atrial model with AF electrical remodelling. This dataset contains the labelled surface meshes, output to the CemrgApp software, which in turn are utilised as inputs for the electrophisiological simulations. The dataset is split into 100 folders labelled M1 to M100. Included in file `Cases_and_Users_Paths.csv` is the pairs for each of the comparisons, whether inter- or intra-observer variability. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/7433014 |
| Title | Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models |
| Description | Abstract Background: Current ablation therapy for atrial fibrillation is sub-optimal and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more inter-individual variability. Methods: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent), undergoing first ablation. Patients were followed for 1-year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fibre orientation maps, electrical properties and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were post-processed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging and atrial fibrillation simulation metrics. Results: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging and simulation stress tests (average ten-fold cross-validation area under the curve 0.85 ± 0.09, recall 0.80 ± 0.13, precision 0.74 ± 0.13) outperformed those trained to history and imaging (area under the curve 0.66 ± 0.17), or history alone (area under the curve 0.61 ± 0.14). Conclusion: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalise selection for atrial fibrillation ablation. Dataset Description: We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. We also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA. More details on the coordinate construction are given in our manuscript and https://www.ncbi.nlm.nih.gov/pubmed/31026761. These coordinates can be used for registering datasets. Publication: https://pubmed.ncbi.nlm.nih.gov/35089057/ |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/5801336 |
| Title | Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models |
| Description | Dataset Description: We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | We have had 1000+ downloads from the data base. This is a massive number for cardiac modelling |
| URL | https://zenodo.org/record/5801337#.Yh0pZJPP0XV |
| Title | Virtual cohort of 1000 synthetic heart meshes from adult human healthy population |
| Description | Dataset Description: We present a database of four-chamber heart models derived from a statistical shape model (SSM) suitable for electro-mechanical (EM) simulations. Our database consists of 1000 four-chamber heart models generated from end-diastolic CT-derived meshes (available in the repository called ("Virtual cohort of adult healthy four-chamber heart meshes from CT images"). These meshes were used for EM simulations. The weights of the SSM are also provided. Cardiac meshes: To build the SSM, we rigidly aligned the CT cohort and extracted the surfaces, representing them asdeRham currents. The registration between meshes and computation of the average shape was done using a Large Deformation Diffeomorphic Metric Mapping method. The deformation functions depend on a set of uniformly distributed control points in which the shapes are embedded, and on the deformation vectors attached to these points. It is in this spatial field of deformation vectors (one per each control point) where the Principal Component Analysis (PCA) is applied. Case #20 of the CT cohort was not included. More information on the details can be found in Supplement 3 of the reference paper. We created this cohort by modifying the weight of the modes explaining 90%of the variance in shape (corresponding to modes 1 to 9) within 2 standard deviations (SD) of each mode added to the average mesh. The elements of all the meshes are labelled as follows: Left ventricle myocardium Right ventricle myocardium Left atrium myocardium Right atrium myocardium Aorta wall Pulmonary artery wall Mitral valve plane Tricuspid valve plane Aortic valve plane Pulmonary valve plane Left atrium appendage "inlet" Left superior pulmonary vein inlet Left inferior pulmonary vein inlet Right inferior pulmonary vein inlet Right superior pulmonary vein inlet Superior vena cava inlet Inferior vena cava inlet Left atrial appendage border Right inferior pulmonary vein border Left inferior pulmonary vein border Left superior pulmonary vein border Right superior pulmonary vein border Superior vena cava border Inferior vena cava border Each zipped folder contains 25 meshes and the weights of modes used to construct them for each mesh, A VTK file for each mesh (in ASCII) contains an UNSTRUCTURED GRID with the following fields: POINTS, with the coordinates of the points in mm. CELL_TYPES, having all of the points the value 10 since they are tetrahedra. CELLS, with the indices of the vertices of every element. CELL_DATA, corresponding to the meshing tags. In addition, three descriptive files are included: Normalized_explained_variance.csv contains the percentages of variance explained by each of the 18 modes generated from PCA. Mode_standard_deviation.csv contains absolute standard deviations of each of the 18 modes. Eigenvectors.csv contains the directions of maximum shape variability within the shape population. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4506929 |
| Title | Virtual cohort of adult healthy four-chamber heart meshes from CT images |
| Description | Dataset Description: We present the first database of four-chamber healthy heart models suitable for electro-mechanical (EM) simulations. Our database consists of twenty four-chamber heart models generated from end-diastolic CT acquired from patients who went to the emergency room with acute chest pains. Since no cardiac conditions were detected in follow-up, these patients were taken as representative of "healthy" (or asymptomatic) hearts. These meshes were used for EM simulations and to build a statistical shape model (SSM). The output of the simulations and the weights of the SSM are also provided. Cardiac meshes: We segmented end-diastolic CT. The segmentation was then upsampled and smoothed. The final multi-label segmentation was used to generate a tetrahedral mesh. The resulting meshes had an average edge length of 1 mm. The elements of all the twenty meshes are labelled as follows: Left ventricle myocardium Right ventricle myocardium Left atrium myocardium Right atrium myocardium Aorta wall Pulmonary artery wall Mitral valve plane Tricuspid valve plane Aortic valve plane Pulmonary valve plane Left atrium appendage "inlet" Left superior pulmonary vein inlet Left inferior pulmonary vein inlet Right inferior pulmonary vein inlet Right superior pulmonary vein inlet Superior vena cava inlet Inferior vena cava inlet Left atrial appendage border Right inferior pulmonary vein border Left inferior pulmonary vein border Left superior pulmonary vein border Right superior pulmonary vein border Superior vena cava border Inferior vena cava border Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80° to -60°, respectively. We defined a system of universal ventricular coordinates on the meshes: an apico-basal coordinate (Z) varying continuously from 0 at the apex to 1 at the base (defined with the mitral and tricuspid valve); a transmural coordinate (\(\rho\)) varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate (\(\phi\)) varying continuously from - p at the left ventricular free wall, 0 at the septum and then back to + p at the left ventricular free wall; intra-ventricular coordinate (V) defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -10. We provide a zipped folder for each mesh, A VTK file for each mesh was included (in ASCII) as an UNSTRUCTURED GRID. In all the cases the following fields were included: POINTS, with the coordinates of the points in mm. CELL_TYPES, having all of the points the value 10 since they are tetrahedra. CELLS, with the indices of the vertices of every element. CELL_DATA, corresponding to the meshing tags. VECTORS, with the directions of the fibres. POINT_DATA, with four LOOKUP_TABLE subfields corresponding to the UVC in the order \(\rho\), \(\phi\), Z and V. Cardiac simulations: For the cardiac EM simulations we used CARP (Cardiac Arrhythmia Research Package). We used the reaction-eikonal model for electrophysiology, stimulating as initial condition the bottom third (Z < 0.33) of the endocardium. We simulated the large deformation mechanics in a Lagrangian reference system. The ventricular myocardium was modelled as a hyperelastic transversely isotropic material with Guccione's strain energy function. The remaining tissues were modelled as non-contracting neo-Hookean materials. Simulations of meshes #09 and #10 failed to converge. Details on the specific parametrisation can be found in the supplements of the reference paper. We provide comma-separated-values files with the output of the simulations used in the reference paper for validation purposes. The simulations of the cases that did not converge were not included. The acronyms used in the names of columns are: EDP: End-diastolic pressure EDV: End-diastolic volume Myo_vol: Myocardial volume of the ventricle (as sum of its elements) ESV: End-systolic volume SV: Stroke volume EF: Ejection fraction V1: Volume at time of peak flow EF1: First-Phase Ejection Fraction ESP: End-systolic pressure dPdtmax: Maximum increase of pressure dPdtmin: Maximum decrease of pressure PeakP: Peak pressure tpeak: Time to peak pressure ET: Ejection time ICT: Isovolumic contraction time IRT: Isovolumic relaxation time tsys: Duration of systole QRS: QRS duration AT1090: Time taken to activate from 10% to 90% of the mesh AT: Activation time of the left ventricle Besides the output value name, in each column is specified the ventricle where that output was extracted from with the suffixes "_LV" or "_RV". Statistical shape model: All the meshes but #20 were used to build a statistical shape model of four-chambers cardiac meshes. In short, we rigidly aligned the meshes and extracted the surfaces, representing them as deRham currents. The registration between meshes and computation of the average shape (also called atlas or template) was done using a Large Deformation Diffeomorphic Metric Mapping method. Each one of the meshes can be approximated as a linear combination of the shape modes, extracted using Principal Component Analysis on the space where the meshes are located. More details on the Statistical Shape Model are provided in the supplement of the reference paper. The average heart and extreme cases are provided in the repository named "Virtual cohort of extreme and average four-chamber heart meshes from statistical shape model". We have added 1000 more meshes from the same statistical shape model, modifying the weights from the PCA randomly within the 2SD range. These meshes are provided in the repository named "Virtual cohort of 1000 synthetic heart meshes from the adult human healthy population". We provide the weights of the modes for each of the 19 meshes in a comma-separated-values file. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4590293 |
| Title | Virtual cohort of adult healthy four-chamber heart meshes from CT images |
| Description | Dataset Description: We present the first database of four-chamber healthy heart models suitable for electro-mechanical (EM) simulations. Our database consists of twenty four-chamber heart models generated from end-diastolic CT acquired from patients who went to the emergency room with acute chest pains. Since no cardiac conditions were detected in follow-up, these patients were taken as representative of "healthy" (or asymptomatic) hearts. These meshes were used for EM simulations and to build a statistical shape model (SSM). The output of the simulations and the weights of the SSM are also provided. Cardiac meshes: We segmented end-diastolic CT. The segmentation was then upsampled and smoothed. The final multi-label segmentation was used to generate a tetrahedral mesh. The resulting meshes had an average edge length of 1 mm. The elements of all the twenty meshes are labelled as follows: Left ventricle myocardium Right ventricle myocardium Left atrium myocardium Right atrium myocardium Aorta wall Pulmonary artery wall Mitral valve plane Tricuspid valve plane Aortic valve plane Pulmonary valve plane Left atrium appendage "inlet" Left superior pulmonary vein inlet Left inferior pulmonary vein inlet Right inferior pulmonary vein inlet Right superior pulmonary vein inlet Superior vena cava inlet Inferior vena cava inlet Left atrial appendage border Right inferior pulmonary vein border Left inferior pulmonary vein border Left superior pulmonary vein border Right superior pulmonary vein border Superior vena cava border Inferior vena cava border Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80° to -60°, respectively. We defined a system of universal ventricular coordinates on the meshes: an apico-basal coordinate (Z) varying continuously from 0 at the apex to 1 at the base (defined with the mitral and tricuspid valve); a transmural coordinate (\(\rho\)) varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate (\(\phi\)) varying continuously from - p at the left ventricular free wall, 0 at the septum and then back to + p at the left ventricular free wall; intra-ventricular coordinate (V) defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -10. We provide a zipped folder for each mesh, A VTK file for each mesh was included (in ASCII) as an UNSTRUCTURED GRID. In all the cases the following fields were included: POINTS, with the coordinates of the points in mm. CELL_TYPES, having all of the points the value 10 since they are tetrahedra. CELLS, with the indices of the vertices of every element. CELL_DATA, corresponding to the meshing tags. VECTORS, with the directions of the fibres. POINT_DATA, with four LOOKUP_TABLE subfields corresponding to the UVC in the order \(\rho\), \(\phi\), Z and V. Cardiac simulations: For the cardiac EM simulations we used CARP (Cardiac Arrhythmia Research Package). We used the reaction-eikonal model for electrophysiology, stimulating as initial condition the bottom third (Z < 0.33) of the endocardium. We simulated the large deformation mechanics in a Lagrangian reference system. The ventricular myocardium was modelled as a hyperelastic transversely isotropic material with Guccione's strain energy function. The remaining tissues were modelled as non-contracting neo-Hookean materials. Simulations of meshes #09 and #10 failed to converge. Details on the specific parametrisation can be found in the supplements of the reference paper. We provide comma-separated-values files with the output of the simulations used in the reference paper for validation purposes. The simulations of the cases that did not converge were not included. The acronyms used in the names of columns are: EDP: End-diastolic pressure EDV: End-diastolic volume Myo_vol: Myocardial volume of the ventricle (as sum of its elements) ESV: End-systolic volume SV: Stroke volume EF: Ejection fraction V1: Volume at time of peak flow EF1: First-Phase Ejection Fraction ESP: End-systolic pressure dPdtmax: Maximum increase of pressure dPdtmin: Maximum decrease of pressure PeakP: Peak pressure tpeak: Time to peak pressure ET: Ejection time ICT: Isovolumic contraction time IRT: Isovolumic relaxation time tsys: Duration of systole QRS: QRS duration AT1090: Time taken to activate from 10% to 90% of the mesh AT: Activation time of the left ventricle Besides the output value name, in each column is specified the ventricle where that output was extracted from with the suffixes "_LV" or "_RV". Statistical shape model: All the meshes but #20 were used to build a statistical shape model of four-chambers cardiac meshes. In short, we rigidly aligned the meshes and extracted the surfaces, representing them as deRham currents. The registration between meshes and computation of the average shape (also called atlas or template) was done using a Large Deformation Diffeomorphic Metric Mapping method. Each one of the meshes can be approximated as a linear combination of the shape modes, extracted using Principal Component Analysis on the space where the meshes are located. More details on the Statistical Shape Model are provided in the supplement of the reference paper. The average heart and extreme cases are provided in the repository named "Virtual cohort of extreme and average four-chamber heart meshes from statistical shape model". We have added 1000 more meshes from the same statistical shape model, modifying the weights from the PCA randomly within the 2SD range. These meshes are provided in the repository named "Virtual cohort of 1000 synthetic heart meshes from the adult human healthy population". We provide the weights of the modes for each of the 19 meshes in a comma-separated-values file. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4590294 |
| Title | Virtual cohort of extreme and average four-chamber heart meshes from statistical shape model |
| Description | Dataset Description: We present a database of four-chamber heart models derived from a statistical shape model (SSM) suitable for electro-mechanical (EM) simulations. Our database consists of 39 four-chamber heart models generated from end-diastolic CT-derived meshes (available in the repository called ("Virtual cohort of adult healthy four-chamber heart meshes from CT images"). These meshes were used for EM simulations. The output of the simulations and the weights of the SSM are also provided. Cardiac meshes: To build the SSM, we rigidly aligned the CT cohort and extracted the surfaces, representing them as deRham currents. The registration between meshes and computation of the average shape was done using a Large Deformation Diffeomorphic Metric Mapping method. The deformation functions depend on a set of uniformly distributed control points in which the shapes are embedded, and on the deformation vectors attached to these points. It is in this spatial field of deformation vectors (one per each control point) where the Principal Component Analysis is applied. Case #20 of the CT cohort was not included. More information on the details can be found in Supplement 3 of the reference paper. We created two extra cohorts by modifying the weight of the modes explaining 90%of the variance in shape (corresponding to modes 1 to 9). We created these meshes with either ±2 or ±3 standard deviations (SD) of each mode added to the average mesh (extreme2 and extreme3 cohorts respectively). We also created two additional meshes with ±1 SD for mode 2 (extreme1 cohort). The elements of all the meshes are labelled as follows: Left ventricle myocardium Right ventricle myocardium Left atrium myocardium Right atrium myocardium Aorta wall Pulmonary artery wall Mitral valve plane Tricuspid valve plane Aortic valve plane Pulmonary valve plane Left atrium appendage "inlet" Left superior pulmonary vein inlet Left inferior pulmonary vein inlet Right inferior pulmonary vein inlet Right superior pulmonary vein inlet Superior vena cava inlet Inferior vena cava inlet Left atrial appendage border Right inferior pulmonary vein border Left inferior pulmonary vein border Left superior pulmonary vein border Right superior pulmonary vein border Superior vena cava border Inferior vena cava border Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80° to -60°, respectively. We defined a system of universal ventricular coordinates on the meshes: an apico-basal coordinate (Z) varying continuously from 0 at the apex to 1 at the base (defined with the mitral and tricuspid valve); a transmural coordinate (\(\rho\)) varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate (\(\phi\)) varying continuously from - p at the left ventricular free wall, 0 at the septum and then back to + p at the left ventricular free wall; intra-ventricular coordinate (V) defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -10. We provide a zipped folder for each mesh, A VTK file for each mesh was included (in ASCII) as an UNSTRUCTURED GRID. In all the cases the following fields were included: POINTS, with the coordinates of the points in mm. CELL_TYPES, having all of the points the value 10 since they are tetrahedra. CELLS, with the indices of the vertices of every element. CELL_DATA, corresponding to the meshing tags. VECTORS, with the directions of the fibres. POINT_DATA, with four LOOKUP_TABLE subfields corresponding to the UVC in the order \(\rho\), \(\phi\), Z and V. We provide the average mesh, and the extreme1, extreme2 and extreme3 cohorts. These correspond to the files of the form modeX±1SD for extreme 1, modeX±2SD for extreme2 and modeX±3SD for extreme3. These meshes have been used to interpret the anatomical meaning of modifying each mode. Cardiac simulations: For the cardiac EM simulations we used CARP (Cardiac Arrhythmia Research Package). We used the reaction-eikonal model for electrophysiology, stimulating as initial condition the bottom third (Z < 0.33) of the endocardium. We simulated the large deformation mechanics in a Lagrangian reference system. The ventricular myocardium was modelled as a hyperelastic transversely isotropic material with Guccione's strain energy function. The remaining tissues were modelled as non-contracting neo-Hookean materials. Simulations diverged for cases mode2-3SD, mode3+3SD, mode6-3SD, mode9-3SD and mode2-2SD . Details on the specific parametrisation can be found in the supplements of the reference paper. We provide comma-separated-values files with the output of the simulations used in the reference paper for validation purposes. The simulations of the cases that did not converge were not included. The acronyms used in the names of columns are: EDP: End-diastolic pressure EDV: End-diastolic volume Myo_vol: Myocardial volume of the ventricle (as sum of its elements) ESV: End-systolic volume SV: Stroke volume EF: Ejection fraction V1: Volume at time of peak flow EF1: First-Phase Ejection Fraction ESP: End-systolic pressure dPdtmax: Maximum increase of pressure dPdtmin: Maximum decrease of pressure PeakP: Peak pressure tpeak: Time to peak pressure ET: Ejection time ICT: Isovolumic contraction time IRT: Isovolumic relaxation time tsys: Duration of systole QRS: QRS duration AT1090: Time taken to activate from 10% to 90% of the mesh AT: Activation time of the left ventricle Besides the output value name, in each column is specified the ventricle where that output was extracted from with the suffixes "_LV" or "_RV". SSM weights: Each one of the meshes can be approximated as a linear combination of the shape modes, extracted using Principal Component Analysis on the space where the meshes are located. We provide the weights for each mesh in a comma-separated-values file. We have added 1000 more meshes from the same statistical shape model, modifying the weights from the PCA randomly within the 2SD range. These meshes are provided in the repository named "Virtual cohort of 1000 synthetic heart meshes from the adult human healthy population". |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4593738 |
| Title | Virtual cohort of extreme and average four-chamber heart meshes from statistical shape model |
| Description | Dataset Description: We present a database of four-chamber heart models derived from a statistical shape model (SSM) suitable for electro-mechanical (EM) simulations. Our database consists of 39 four-chamber heart models generated from end-diastolic CT-derived meshes (available in the repository called ("Virtual cohort of adult healthy four-chamber heart meshes from CT images"). These meshes were used for EM simulations. The output of the simulations and the weights of the SSM are also provided. Cardiac meshes: To build the SSM, we rigidly aligned the CT cohort and extracted the surfaces, representing them as deRham currents. The registration between meshes and computation of the average shape was done using a Large Deformation Diffeomorphic Metric Mapping method. The deformation functions depend on a set of uniformly distributed control points in which the shapes are embedded, and on the deformation vectors attached to these points. It is in this spatial field of deformation vectors (one per each control point) where the Principal Component Analysis is applied. Case #20 of the CT cohort was not included. More information on the details can be found in Supplement 3 of the reference paper. We created two extra cohorts by modifying the weight of the modes explaining 90%of the variance in shape (corresponding to modes 1 to 9). We created these meshes with either ±2 or ±3 standard deviations (SD) of each mode added to the average mesh (extreme2 and extreme3 cohorts respectively). We also created two additional meshes with ±1 SD for mode 2 (extreme1 cohort). The elements of all the meshes are labelled as follows: Left ventricle myocardium Right ventricle myocardium Left atrium myocardium Right atrium myocardium Aorta wall Pulmonary artery wall Mitral valve plane Tricuspid valve plane Aortic valve plane Pulmonary valve plane Left atrium appendage "inlet" Left superior pulmonary vein inlet Left inferior pulmonary vein inlet Right inferior pulmonary vein inlet Right superior pulmonary vein inlet Superior vena cava inlet Inferior vena cava inlet Left atrial appendage border Right inferior pulmonary vein border Left inferior pulmonary vein border Left superior pulmonary vein border Right superior pulmonary vein border Superior vena cava border Inferior vena cava border Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80° to -60°, respectively. We defined a system of universal ventricular coordinates on the meshes: an apico-basal coordinate (Z) varying continuously from 0 at the apex to 1 at the base (defined with the mitral and tricuspid valve); a transmural coordinate (\(\rho\)) varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate (\(\phi\)) varying continuously from - p at the left ventricular free wall, 0 at the septum and then back to + p at the left ventricular free wall; intra-ventricular coordinate (V) defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -10. We provide a zipped folder for each mesh, A VTK file for each mesh was included (in ASCII) as an UNSTRUCTURED GRID. In all the cases the following fields were included: POINTS, with the coordinates of the points in mm. CELL_TYPES, having all of the points the value 10 since they are tetrahedra. CELLS, with the indices of the vertices of every element. CELL_DATA, corresponding to the meshing tags. VECTORS, with the directions of the fibres. POINT_DATA, with four LOOKUP_TABLE subfields corresponding to the UVC in the order \(\rho\), \(\phi\), Z and V. We provide the average mesh, and the extreme1, extreme2 and extreme3 cohorts. These correspond to the files of the form modeX±1SD for extreme 1, modeX±2SD for extreme2 and modeX±3SD for extreme3. These meshes have been used to interpret the anatomical meaning of modifying each mode. Cardiac simulations: For the cardiac EM simulations we used CARP (Cardiac Arrhythmia Research Package). We used the reaction-eikonal model for electrophysiology, stimulating as initial condition the bottom third (Z < 0.33) of the endocardium. We simulated the large deformation mechanics in a Lagrangian reference system. The ventricular myocardium was modelled as a hyperelastic transversely isotropic material with Guccione's strain energy function. The remaining tissues were modelled as non-contracting neo-Hookean materials. Simulations diverged for cases mode2-3SD, mode3+3SD, mode6-3SD, mode9-3SD and mode2-2SD . Details on the specific parametrisation can be found in the supplements of the reference paper. We provide comma-separated-values files with the output of the simulations used in the reference paper for validation purposes. The simulations of the cases that did not converge were not included. The acronyms used in the names of columns are: EDP: End-diastolic pressure EDV: End-diastolic volume Myo_vol: Myocardial volume of the ventricle (as sum of its elements) ESV: End-systolic volume SV: Stroke volume EF: Ejection fraction V1: Volume at time of peak flow EF1: First-Phase Ejection Fraction ESP: End-systolic pressure dPdtmax: Maximum increase of pressure dPdtmin: Maximum decrease of pressure PeakP: Peak pressure tpeak: Time to peak pressure ET: Ejection time ICT: Isovolumic contraction time IRT: Isovolumic relaxation time tsys: Duration of systole QRS: QRS duration AT1090: Time taken to activate from 10% to 90% of the mesh AT: Activation time of the left ventricle Besides the output value name, in each column is specified the ventricle where that output was extracted from with the suffixes "_LV" or "_RV". SSM weights: Each one of the meshes can be approximated as a linear combination of the shape modes, extracted using Principal Component Analysis on the space where the meshes are located. We provide the weights for each mesh in a comma-separated-values file. We have added 1000 more meshes from the same statistical shape model, modifying the weights from the PCA randomly within the 2SD range. These meshes are provided in the repository named "Virtual cohort of 1000 synthetic heart meshes from the adult human healthy population". |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4593739 |
| Title | text_figures_tables.zip from Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats |
| Description | Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function (R2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function.This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/text_figures_tables_zip_from_Predicting_left_ventricular_contractil... |
| Title | text_figures_tables.zip from Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats |
| Description | Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function (R2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function.This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/text_figures_tables_zip_from_Predicting_left_ventricular_contractil... |
| Title | text_figures_tables.zip from Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats. 27 April 2020 |
| Description | Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function (R2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function.This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/text_figures_tables_zip_from_Predicting_left_ventricular_contractil... |
| Description | Computational Statistics |
| Organisation | University of Nottingham |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We are providing challenging problems and simualtion results. |
| Collaborator Contribution | Richard Wilkinson and his team are developing methods for improved calibration and forecasting. |
| Impact | Coveney S, Corrado C, Oakley JE, Wilkinson RD, Niederer SA, Clayton RH. Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators. Front Physiol. 2021 Jul 22;12:693015. doi: 10.3389/fphys.2021.693015. Erratum in: Front Physiol. 2021 Oct 04;12:765622. PMID: 34366883; PMCID: PMC8339909. Coveney S, Roney CH, Corrado C, Wilkinson RD, Oakley JE, Niederer SA, Clayton RH. Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds. Sci Rep. 2022 Oct 4;12(1):16572. doi: 10.1038/s41598-022-20745-z. PMID: 36195766; PMCID: PMC9532401. Corrado C, Roney CH, Razeghi O, Lemus JAS, Coveney S, Sim I, Williams SE, O'Neill MD, Wilkinson RD, Clayton RH, Niederer SA. Quantifying the impact of shape uncertainty on predicted arrhythmias. Comput Biol Med. 2023 Feb;153:106528. doi: 10.1016/j.compbiomed.2022.106528. Epub 2023 Jan 3. PMID: 36634600. Strocchi M, Longobardi S, Augustin CM, Gsell MAF, Petras A, Rinaldi CA, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA. Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators. PLoS Comput Biol. 2023 Jun 26;19(6):e1011257. doi: 10.1371/journal.pcbi.1011257. PMID: 37363928; PMCID: PMC10328347. |
| Start Year | 2021 |
| Description | Turing |
| Organisation | Alan Turing Institute |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We have worked with researchers at the Turing (Professor Chris Oates, Professor Mark Girolami and Jon Cockayne) on this BHF project. |
| Collaborator Contribution | They have employed the people we are collaborating with. |
| Impact | We received a BHF-Turing grant and a BHF programme grant. We have submitted two conference abstracts and have two papers in draft. |
| Start Year | 2018 |
| Description | University of Sheffield |
| Organisation | University of Sheffield |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Collaborating on running uncertainty quantification workshops and jointly funded on this grant. |
| Collaborator Contribution | collaborating on grant |
| Impact | We have run multiple workshops together, done media presentations together and written papers together |
| Start Year | 2017 |
| Title | CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research. |
| Description | The Cardiac Electro-Mechanics Research Group Application (CemrgApp) is a platform with custom image processing and computer vision toolkits for applying statistical, machine learning, and simulation approaches to cardiovascular data. CemrgApp provides an integrated environment, where cardiac data visualisation and workflow prototyping are presented through a common user friendly graphical interface. CemrgApp at present supports: |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | CemrgApp has provided a common platform for medical image analysts and cardiologists to cooperate on various applications, with a focus on translation. It has led to a retrospective and now preprocedural planning in a prospective clinical trial (Fire and Ice, Gov Identifier: NCT03706677), provided the foundation for research contracts with industrial partners (Medtronic, EBR, Abbott), and contributed to an EPSRC project grant, NIH R01, ERC fellowship, BHF fellowship, MRC fellowship, BHF programme grant, and Wellcome programme, building a large cohort of patient-specific atrial models for computational modelling studies. The app has also delivered ad hoc solutions to international cardiac research groups based at Stanford's mechanical engineering department, the Catholic University of Louvain in Belgium, the Amsterdam UMC, bioengineers at the University of Washington, cardiologists at the University of Oxford, and the department of medical biophysics at the University of Toronto. Research software from the AI centre has also been planned to become available in the app. Overall, CemrgApp has provided support for more than 30 journal, conference, and abstract publications. |
| Description | 2021 Invited speaker, AI and ML in Cardiovascular CT 2.0, An SCCT Innovation webinar series |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Talk on analyzing cardiac CT |
| Year(s) Of Engagement Activity | 2021 |
| Description | 2021 Invited speaker, Mechanistic Machine Learning and Digital Twins Conference, San Diego |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Overview of our paper on digital twins |
| Year(s) Of Engagement Activity | 2021 |
| Description | 2022 Keynote, UKRI Cyber Physical Interface Research Skills Panel, online |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Policymakers/politicians |
| Results and Impact | Talk on digital twins |
| Year(s) Of Engagement Activity | 2022 |
| Description | 2022 Seminar, Georgia Institute of Technology, online |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Other audiences |
| Results and Impact | Lab overview |
| Year(s) Of Engagement Activity | 2022 |
| Description | 2022 Seminar, Harvard, Boston Children's Hospital, Boston |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Talk on digital twins |
| Year(s) Of Engagement Activity | 2022 |
| Description | 2022 Seminar, University of Toronto, online |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Lab overview |
| Year(s) Of Engagement Activity | 2022 |
| Description | 2022 Seminar, Consortium for Electrocardiographic Imaging, online |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Lab overview |
| Year(s) Of Engagement Activity | 2022 |
| Description | 2022 The Cardiology Science Lunch Berlin, Charite, Berlin |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | lab overview |
| Year(s) Of Engagement Activity | 2022 |
| Description | 2022 HRS LOCAL AREA STRAINS FROM RETROSPECTIVE GATED COMPUTED TOMOGRAPHY IMAGING TO DETECT ATRIAL FIBRILLATION |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Poster act clinical conference |
| Year(s) Of Engagement Activity | 2022 |
| Description | 2023 Invited Speaker, Cardiac Physiome Meeting, Los Angeles |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Talk on digital twins, lead to UCSD post doc approaching me to discuss projects |
| Year(s) Of Engagement Activity | 2023 |
| Description | 2023 Invited Speaker, Digital Health and Modelling Conference, London |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Other audiences |
| Results and Impact | Talk on digital twins |
| Year(s) Of Engagement Activity | 2023 |
| Description | 2023 Invited Speaker, Gordon Research Conference, Galveston Texas, US |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Talk on digital twins, lead to conversations about Norway-UK meeting |
| Year(s) Of Engagement Activity | 2023 |
| Description | 2023 Invited Speaker, Heart Rhythm, New Orleans |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | talk on digital twins |
| Year(s) Of Engagement Activity | 2023 |
| Description | 2023 Invited Speaker, NumeriCore Seminar, Graz |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Industry/Business |
| Results and Impact | talk on use of the CARP simulator |
| Year(s) Of Engagement Activity | 2024 |
| Description | 2023 Invited speaker, Data-driven mechanistic models of complex biomedical systems, Birmingham. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | Talk on digital twins |
| Year(s) Of Engagement Activity | 2024 |
| Description | 2023 Invited speaker, Structural Dynamics workshop, Sheffield. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | talk on digital twins |
| Year(s) Of Engagement Activity | 2024 |
| Description | 2023 Keynote Speaker, Informatics for Life workshop, Heidelberg |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Professional Practitioners |
| Results and Impact | talk on digital twins |
| Year(s) Of Engagement Activity | 2023 |
| Description | 2023 Keynote Speaker, Welsh Digital Twin Network, Swansea |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Industry/Business |
| Results and Impact | Talk on digital twins and the national digital twin network plus |
| Year(s) Of Engagement Activity | 2023 |
| Description | 2023 Invited Speaker, Gaussian Process Summer School, Manchester |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | talk on use of machine learning in digital twins |
| Year(s) Of Engagement Activity | 2023 |
| Description | 2023 Cardiac Physiome Workshop "Sensitivity analysis of electrode location on ECG signals" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Ludovica gave a presentation |
| Year(s) Of Engagement Activity | 2023 |
| Description | 2024 Invited Speaker, Designing the Future of Digital Twins in Healthcare, Lancaster |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Other audiences |
| Results and Impact | talk on digital twins |
| Year(s) Of Engagement Activity | 2024 |
| Description | 2024 Invited Speaker, NASA-Turing- National Oceanography Centre workshop |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | talk about digital twins to NASA |
| Year(s) Of Engagement Activity | 2024 |
| Description | 2024 Invited Speaker, Norwegian - UK Digital Twin Collaboration, London |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | talk on digital twins |
| Year(s) Of Engagement Activity | 2024 |
| Description | AHA 2023 poster, title: Novel Regional Analysis of Left Atrial Strain From Computed Tomography Separates Patients With Persistent versus Paroxysmal Atrial Fibrillation |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Update on thesis work |
| Year(s) Of Engagement Activity | 2023 |
| Description | ARCHER2 Celebration of Science "A multi-scale analysis of the impact of measurement and physiological uncertainty on electrocardiograms |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Ludovica presented her results |
| Year(s) Of Engagement Activity | 2024 |
| Description | FMIH poster, title: Optimisation of Left Atrial Feature Tracking Using Retrospective Gated Computed Tomography Images |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Update of results |
| Year(s) Of Engagement Activity | 2021 |
| Description | HRS 2023 REGIONAL DIFFERENCES IN ATRIAL FIBER STRAINS IN HEART FAILURE PATIENTS WITH AND WITHOUT ATRIAL FIBRILLATION |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Update on thesis work |
| Year(s) Of Engagement Activity | 2023 |
| Description | Isaac Newton Meeting |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Organised meeting and presentation at Isaac Newton Meeting |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.newton.ac.uk/event/fht |
| Description | John Hopkins University |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited talk John Hopkins University |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://icm.jhu.edu/events/steven-niederer-kings-college-london-applying-cardiac-modelling-to-study-... |
| Description | Lange Symposium |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited presentation at Lange Symposium |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.dhzb.de/fileadmin/user_upload/relaunch/02_medizin_pflege/AHF/Langesymposium/2019/Program... |
| Description | Math 2 Product (M2P 2023) "Sensitivity analysis of electrode location on ECG signals" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Ludovica gave a presentation |
| Year(s) Of Engagement Activity | 2023 |
| Description | Mox invited talk |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | invited presentation to numerics group in Milan |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://mox.polimi.it/elenco-seminari/?id_evento=1919&t=763721&ricerca= |
| Description | Murdoch Children's Research Institute |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited presentation at Murdoch Children's Research Institute, |
| Year(s) Of Engagement Activity | 2019 |
| Description | Oslo University Hospital |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited presentation |
| Year(s) Of Engagement Activity | 2020 |
| Description | Oxford talk |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited talk at physiology department oxford |
| Year(s) Of Engagement Activity | 2019 |
| Description | Pfizer |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Invited talk at Pfizer |
| Year(s) Of Engagement Activity | 2019 |
| Description | Philips |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Invited talk at Philips to discuss how we are developing digital twins. |
| Year(s) Of Engagement Activity | 2019 |
| Description | Prince Alfred Hospital |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited presentation at Prince Alfred Hospital |
| Year(s) Of Engagement Activity | 2019 |
| Description | Simula talk |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | I gave a presentation at Simula a norwegian research institute to about 40+ researchers. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.simula.no/simula-seminars-scientific-computing |
| Description | St Vincent's Hospital |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited talk at St Vincent's Hospital |
| Year(s) Of Engagement Activity | 2019 |
| Description | TRM Lugano |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited presentation to biomedical engineering and clinical research workshop. |
| Year(s) Of Engagement Activity | 2019 |
| Description | UCSD |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited presentation at UCSD Biomedical Engineering department |
| Year(s) Of Engagement Activity | 2019 |
| Description | University of Auckland |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited presentation at University of Auckland |
| Year(s) Of Engagement Activity | 2019 |
| Description | Victor Chang Sydney |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited presentation at victor chang institute |
| Year(s) Of Engagement Activity | 2019 |
| Description | Yale |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Invited presentation at the Biomedical Engineering deparmtent in Yale |
| Year(s) Of Engagement Activity | 2019 |
| Description | Youtube |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | youtube interview for Newton Institute meeting |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.youtube.com/watch?v=MSGaojtXcEA |