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This protocol demonstrates the workflow of the SILICOFCM platform for automatically generating a parametric model of the left ventricle from patient-specific ultrasound images by applying a multi-scale electromechanical model of the heart. This platform enables in silico clinical trials intended to reduce real clinical trials and maximize positive therapeutic outcomes.
The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.
The rapid development of information technologies, simulation software packages, and medical devices in recent years provides the opportunity for collecting a large amount of clinical information. Creating comprehensive and detailed computational tools has, therefore, become essential to process specific information from the abundance of available data.
From the physicians' point of view, it is of paramount importance to distinguish "normal" versus "abnormal" phenotypes in a specific patient to estimate disease progression, therapeutic responses, and future risks. Recent computational models have significantly improved the integrative understanding of the behavior of heart muscles in hypertrophic (HCM) and dilated (DCM) cardiomyopathies1. It is crucial to use a high-resolution, detailed, and anatomically accurate model of whole-heart electrical activity, which necessitates massive computation times, dedicated software, and supercomputers1,2,3. A methodology for a real 3D heart model has been recently developed using a linear elastic and orthotropic material model based on Holzapfel experiments, which can accurately predict the electrical signal transport and displacement field within heart4. The development of novel integrative modeling approaches could be an effective tool for distinguishing the type and severity of symptoms in patients with multigenic disorders and assessing the degree of impairment in normal physical activity.
There are, however, many new challenges for patient-specific modeling. The physical and biological properties of the human heart are not possible to fully determine. Non-invasive measurements usually include noisy data from which it is difficult to estimate specific parameters for the individual patient. Large-scale computation requires a lot of time to run, whereas the clinical time frame is limited. Patient personal data should be managed in such a way that generated metadata can be reused without compromising patient confidentiality. Despite these challenges, multi-scale heart models can include a sufficient level of detail to achieve predictions that closely follow observed transient responses, thereby providing promise for prospective clinical applications.
However, regardless of the substantial scientific effort by multiple research labs and the significant amount of grant support, currently, there is only one commercially available software package for multiscale and whole heart simulations, called SIMULIA Living Heart Model5. It includes dynamic electro-mechanical simulation, refined heart geometry, a blood flow model, and complete cardiac tissue characterization, including passive and active characteristics, fibrous nature, and electrical pathways. This model is targeted for use in personalized medicine, but the active material characterization is based on a phenomenological model introduced by Guccione et al.6,7. Therefore, SIMULIA cannot directly and accurately translate the changes in contractile protein functional characteristics observed in numerous cardiac diseases. These changes are caused by mutations and other abnormalities at the molecular and subcellular levels6. The limited use of SIMULIA software for a small number of applications in clinical practice is a great example of today's struggles in developing higher-level multiscale human heart models. On the other hand, it motivates the development of a new generation of multiscale program packages that can trace the effects of mutations from the molecular to organ scale.
The main aim of electrophysiology of the heart is to determine signal propagation inside the torso and the properties of all compartments4,5,6. The SILICOFCM8 project predicts cardiomyopathy disease development using patient-specific biological, genetic, and clinical imaging data. It is achieved with multiscale modeling of the realistic sarcomeric system, the patient's genetic profile, muscle fiber direction, fluid-structure interaction, and electrophysiology coupling. The effects of left ventricle deformation, mitral valve motion, and complex hemodynamics give detailed functional behavior of the heart conditions in a specific patient.
This article demonstrates the use of the SILICOFCM platform for a parametric model of the left ventricle (LV) generated automatically from patient-specific ultrasound images using a fluid-structure heart model with electromechanical coupling. Apical view and M-mode view analyses of LV were generated with a deep learning algorithm. Then, using the mesh generator, the finite element model was built automatically to simulate different boundary conditions of the full cycle for LV contraction9. On this platform, users can directly visualize the simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses. Input parameters from specific patients are geometry from ultrasound images, velocity profile in the input and output boundary flow conditions for LV, and specific drug therapy (e.g., entresto, digoxin, mavacamten, etc.).
The protocol in this study was approved by the UK National Health Service Health Research Authority North East-Tyne & Wear South Research Ethics Committee with the reference number 18/NE/0318 on 6 February 2019 and was adopted by the Institutional Review Board of each participating center. The study was conducted within the principles of Good Clinical Practice and following the Declaration of Helsinki. Informed consent was obtained from all subjects involved in the study. The patient information is kept anonymous.
1. Workflow for ultrasound M-mode or Apical view DICOM image analysis and LV parameter extraction
NOTE: To begin this protocol, the user must log in to the SILICOFCM platform8 and choose the appropriate workflow (i.e., ultrasound image analysis using M-mode or Apical view). The workflow for ultrasound M-mode DICOM image analysis and LV parameter extraction, for example, involves several steps. The first step is template matching. Templates to be matched include all the relevant necessary borders. It should be emphasized that, for every new dataset, the template should be extracted manually just once for each dataset that corresponds to a specific ultrasound machine. The workflow diagram is presented in Figure 1. The area matching the template will be extracted from the analyzed image after Felsenszwalb's efficient graph-based image segmentation algorithm finds "strong" boundaries that correspond to the borders of the septum and the wall of the LV. Based on these borders and the places where the heart diameters are the largest (corresponds to diameter in diastole) and the smallest (corresponds to diameter in systole), various LV dimensions will be calculated. The user should define whether the view is M-mode or Apical view.
2. PAK finite element solver for fluid-structure (FS) simulations
NOTE: This tool can be used for the finite element analysis of the coupled solid-fluid problems. It supports both strong and loose coupling between solid and fluid. Elements can be hexahedrons or tetrahedrons, with or without an additional node at the center of the element. This solver has built-in material models such as the Holzapfel model, hunter muscle model, etc. The PAK-FS information flow diagram is depicted in Figure 4. It starts from the input file and PAK preprocessor. The PAK preprocessor tool outputs a DAT file, which will serve as an input file for the finite element solver. The final output from the solver is VTK files that include the results of the finite element simulation: velocities, pressures, strains, and stresses in the left ventricle.
3. Determination of ventricular activation sequence from ECG measurement
NOTE: A modified FitzHugh-Nagumo model of the cardiac cell was implemented. The precordial leads were modeled with the standard six electrodes. The potential of the heart was optimized with inverse ECG. Starting from activation in the sinoatrial node (which is a function of time), with heterogeneous action potential through the heart and torso, the user can get electrical activity on the total torso model. The PAK-TORSO information flow diagram is given in Figure 5. The user provides scaling of the torso model in all directions (x,y,z) and ECG signal function. Then the scaled model is created, and its behavior is simulated using the PAK-FS solver. The user provides these input values in a text file. The output of the simulation is a VTK file with the electrical activity of the heart in the torso-embedded environment.
As an example, the workflow for ultrasound M-mode DICOM image analysis and LV parameter extraction is presented in Figure 1. M-mode and apical view could be tested separately or one after another, depending on the parameters of interest. If tested one after another, the results are appended to one common file (separately for systole and diastole phases). If only one view is tested, the values for unknown parameters are taken from the default file input.txt (Figu...
The SILICOFCM project is an in silico clinical trials platform to design virtual patient populations for risk prediction, testing the effects of pharmacological treatment, and reducing animal experiments and human clinical trials. Testing the effects of pharmacological treatment was modeled with prescribed inlet/outlet boundary flow conditions, calcium function, and material wall properties. This platform integrates multiscale methods on the sarcomeric level with whole-heart performance and the functional optimi...
The authors have no conflicts of interest.
This study is supported by the European Union's Horizon 2020 research and innovation program under grant agreement SILICOFCM 777204 and the Ministry of Education, Science and Technological Development of the Republic of Serbia through Contracts No. 451-03-68/2022-14/200107. This article reflects only the authors' views. The European Commission is not responsible for any use that may be made of the information the article contains.
Name | Company | Catalog Number | Comments |
SILICOFCM project | www.silicofcm.eu | open access for registered users |
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