# Towards patient-specific heart modeling for Cardiac Resynchronization Therapy

Farheez Mohamed, Wenfeng Ye, Antoine Simon, Michel Rochette, Mathieu Lederlin, Karim El Houari, Philippe Mabo

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Farheez Mohamed, Wenfeng Ye, Antoine Simon, Michel Rochette, Mathieu Lederlin, et al. Towards patient-specific heart modeling for Cardiac Resynchronization Therapy. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the..., 10.46298/mbj.16184. hal-05191736v2

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# Towards patient-specific heart modeling for Cardiac Resynchronization Therapy

Farheez Mohameda, b*, Wenfeng Yea, Antoine Simonb, Michel Rochettea, Mathieu Lederlinb, Karim El Houaria, Philippe Mabob

a Synopsys Inc., Villeurbanne, France

b Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, Rennes, France

* Corresponding author: farheez.mohamed@ansys.com

Received date: 04/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** heart failure, cardiac resynchronization therapy, cardiac modeling, personalization

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

Cardiovascular diseases are the leading cause of death worldwide (Woodruff et al., 2024). Predicting disease progression and treatment response through a personalized approach is a major challenge nowadays. Patient-specific computational heart models (PSMs) represent an opportunity for personalized diagnosis, patient stratification and treatment planning. For instance, Cardiac Resynchronization Therapy (CRT), although being widely used for heart failure, has a non-negligible rate of non-responders of nearly 30% (Nakai et al. 2022). Using PSM to predict the individual response to the therapy would thus be a great contribution for the treatment optimization. However, model geometrical and functional design are complex and represent a barrier for the investigation and adoption of such strategies. In this paper, we propose a semi-automatic workflow to construct digital twins of patients with heart failure with the objective of assessing CRT response and showcase its feasibility using data from a representative patient. First, medical images were automatically segmented to obtain surfaces defining the patient's heart geometry. Then, model design and simulations were performed using PyAnsys-Heart (Hoeijmakers et al., 2023), a python framework for cardiac modeling tailored for Ansys-LSDYNA, to retrieve metrics of clinical interest. Finally, we compared simulation outputs with clinical routine measurements.

## 2. Methods

This section presents the clinical context of the studied patient and the numerical model.

### 2.1 Clinical data

We consider a patient suffering from heart failure with a left bundle branch block and dilated cardiomyopathy (DCM).

The dataset employed consists of the following:

- End-diastolic CT-scan: pre and post implantation to get the geometry and the leads position of the CRT device

- Cine MRI with a temporal resolution of 26 ms per frame: volume evolution of the ventricles, Stroke volume (SV) and ejection fraction (EF)

- Echocardiography: strain AHA17 evolution on each segment and bullseye plot, pre and post implantation

- Electrocardiogram (ECG): pre and post implantation.

### 2.2 Patient-specific geometry

Patient-specific biventricular geometry is obtained through CT-scan segmentation. It is automatically performed using CardioMerge (Rigal et al. 2024) which outputs the ventricular meshed surfaces. Then, these were modified according to PyAnsys-Heart input surfaces requirements before meshing the BV geometry with tetrahedral elements of 1.5 mm edge length.
## 2.3 Electromechanical model

The model is automatically set up using the workflow proposed in PyAnsys-Heart. The model comprises three major physical components.

- Electrophysiology (EP): the model used eikonal approach including Purkjinje network for the fast propagation at the endocardium, the Ten Tusscher model (Ten Tusscher et al. 2004) is used to represent cellular electrophysiology.

- Mechanics: robin boundary conditions were applied on the pericardium surface, the passive behavior of tissue is described by the Holzapfel-Ogden hyperelastic model (Holzapfel and Ogden, 2009) and the active part is represented by a phenomenological model (Hunter et al. 1997) coupled to the EP calcium transient.

- Circulatory system: a closed-loop Pluijmert model (Pluijmert 2015) was considered to account for blood flow and valves impact on wall motion.

Consequently, electromechanical computation was conducted to compare the numerical and clinical behaviours.

## 3. Results and discussion

Figure 1 shows the clinical left ventricular (LV) volume evolution obtained from the MRI, with QIR-MR (CASIS), the simulated one, and a healthy reference curve adapted to the patient end-diastolic volume (EDV) with a 50% EF. The EDV difference between the clinical data and the model comes from i) the modality from which the volume estimation comes from (CT-scan for the model and MRI for the clinical data) and ii) the basal edge definition in the model compared to QIR-MR. Therefore, an offset MRI curve is displayed to match the model EDV while conserving the EF. Since healthy default material parameters were used, leading to simulated LV volume evolution is far from the clinical one (Table 1) but closer to the healthy reference. Particularly, a simulated SV of 76 mL is inside the healthy range while a 37% EF is below 50% because of the DCM leading to a high EDV. Except for the isovolumetric contraction (IVC) duration longer than 50 ms, isovolumetric relaxation (IVC and IVR), ejection and filling durations are comparable to the healthy reference curve by being inside the healthy ranges (Table 1).

<figure><img src="image_2.png" /><figcaption>Figure 1. Clinical and simulated left ventricular volume evolution</figcaption></figure>

<table><caption>Table 1. Clinical, model and healthy left ventricular volume metrics.</caption><thead><tr><th></th><th>Cine MRI</th><th>Simulation</th></tr></thead><tbody><tr><td>Stroke Volume</td><td>26 mL</td><td>76 mL</td></tr><tr><td>Ejection Fraction</td><td>9%</td><td>37%</td></tr><tr><td>IVC [ms]</td><td>210</td><td>90</td></tr><tr><td>Ejection [ms]</td><td>132</td><td>200</td></tr><tr><td>IVR [ms]</td><td>188</td><td>60</td></tr><tr><td>Filling [ms]</td><td>233</td><td>448</td></tr></tbody></table>

## 4. Conclusions

We propose a semi-automatic workflow to build cardiac digital twins that we successfully applied to a patient with a failing heart. Results are consistent with healthy subjects, but further investigation will be conducted to fit failing heart metrics. The personalization process will consist of tuning the myocardium properties and the Pluijmert system model parameters to reproduce electromechanical behavior of a failing heart.

## References

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Nakai, T., Ikeya, Y., Kogawa, R., & Okumura, Y. (2022). Cardiac resynchronization therapy: Current status and near-future prospect. *Journal of Cardiology*, **79**(3), 352-357. https://doi.org/10.1016/j.jcc.2021.10.021

Hoeijmakers, M., El Houari, K. E., Ye, W., L'Eplattenier, P., Nagy, A., Benson, D., & Rochette, M. (2023, June). pyheart-lib: A Python library for LS-DYNA multi-physics heart simulations. *FIMH 2023. Lecture Notes in Computer Science*, **13958**, 565-574. https://doi.org/10.1007/978-3-031-35302-4_58

Rigal, L., Benali, K., Barré, V., Bougault, M., Bellec, J., De Crevoisier, R., Martins, R., & Simon, A. (2024). Multimodal fusion workflow for target delineation in cardiac radioablation of ventricular tachycardia. *Medical Physics*, **51**(1), 292-305. https://doi.org/10.1002/mp.16613

Ten Tusscher, K. H. W. J., Noble, D., Noble, P. J., & Panfilov, A. V. (2004). A model for human ventricular tissue. *American journal of physiology. Heart and circulatory physiology*, **286**(4), H1573-H1589. https://doi.org/10.1152/ajpheart.00794.2003

Hunter, P.J., Nash, M.P., & Sands, G.B. (1997). Computational electromechanics of the heart. In: A.V. Panfilov and A.V. Holden (eds.), *Computational Biology of the Heart*, John Wiley and Sons Ltd., 12, 345-407.

Pluijmert, M. H. (2015). *How cardiac myofibers keep pace: mathematical modeling of adaptive myofiber reorientation and electromechanics*. (Doctoral Thesis, Maastricht University). Maastricht University. https://doi.org/10.26481/dis.20150924mp

Holzapfel, G. A., & Ogden, R. W. (2009). Constitutive modelling of passive myocardium: a structurally based framework for material characterization. *Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences*, **367**(1902), 3445-3475. https://doi.org/10.1098/rsta.2009.0091

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