Patient-Specific LA Models

• Patient-specific LA models are detailed representations of the left atrium, created from clinical imaging and sophisticated segmentation techniques.
• They integrate fiber mapping, tissue characterization, and biomechanical-electrophysiological simulations to capture regional variations in cardiac function.
• These models guide personalized interventions, such as ablation therapy and valve repair, by accurately simulating patient-specific cardiac mechanics.

Patient-specific left atrial (LA) models are computational or physical representations of left atrium structure and function that are derived directly from clinical images and patient physiology, and are used to paper cardiac biomechanics, electrophysiology, and disease mechanisms at individual resolution. The development of such models integrates high-resolution anatomical imaging, advanced segmentation algorithms, spatial mapping of tissue properties (including fibrosis and fiber orientation), and biomechanical simulation or physical emulation. These models play a central role in investigating the substrate for atrial fibrillation (AF), guiding ablation therapy, and characterizing the impact of interventions or anatomic repair, with increasing emphasis on quantifying regional variation in mechanical or electrical parameters.

1. Imaging and Geometrical Reconstruction

Patient-specific LA modeling workflows commonly begin with acquisition of high-resolution, ECG-gated CT or cardiac MRI scans to capture the 3D geometry of the left atrium, pulmonary veins, mitral valve annulus, and surrounding tissue. Retrospective CT imaging is used for segmentation of anatomical compartments such as LA blood pool, myocardium, and epicardial adipose tissue (EAT), typically employing semi-automated tools for annotation and smoothing. The resulting segmentations are upsampled to isometric voxel resolution and cropped to optimize computational efficiency while preserving anatomical fidelity (Baptiste et al., 21 Oct 2025, Solis-Lemus et al., 2023).

Surface and volumetric meshes are generated using computational geometry toolkits, with downsampling calibrated to balance simulation accuracy and numerical tractability. Universal Atrial Coordinates (UACs)—computed by solving Laplace equations with Dirichlet boundary conditions from operator-placed anatomical landmarks—are widely used to define standardized coordinate systems for mapping fiber orientation, regional analysis, or simulation outputs (Solis-Lemus et al., 2023). The LA mesh is typically partitioned into domains such as anterior, posterior, septal, lateral, and roof regions to support region-specific characterization of deformation, conduction, or tissue properties.

2. Segmentation, Tissue Characterization, and Fiber Mapping

To precisely delineate LA structure and fibrotic burden, patient-specific models incorporate advanced segmentation methods. CNN-based and foundational deep learning architectures such as Med-SAM1/2 provide near real-time segmentation of the LA from 3D late gadolinium-enhanced MRI, with reported average Dice scores of ~0.81–0.84 and Hausdorff distances in the 20–30mm range; sensitivity analyses indicate robustness to prompt location but vulnerability to prompt size alterations (Mehrnia et al., 8 Nov 2024).

Fibrosis mapping is achieved by projecting maximum wall intensity from registered LGE images onto the mesh surface and thresholding using image intensity ratio (IIR), with operator variability in fibrosis labeling measured by ICC (~0.91–0.99 between and within users) (Solis-Lemus et al., 2023). Fiber orientation fields—crucial for simulating anisotropic conduction—are mapped from DTMRI atlases to the mesh using UACs, and inter-operator agreement in orientation has been quantified (60.63%–71.77% above a cosine similarity threshold of 0.924). Model reproducibility studies show that operator differences in anatomical landmark selection and mesh clipping propagate uncertainty through fiber map and simulation outputs, but such errors are comparable to those arising from image resolution or fiber atlas choice.

3. Biomechanical and Electrophysiological Simulation

Patient-specific LA models employ continuum mechanics and electrophysiological frameworks to simulate chamber function and arrhythmogenic substrate. For passive mechanical behavior, a transversely isotropic Guccione material law is used for the myocardium,

$$\Psi(E) = \frac{C}{2}(e^{Q} - 1) + \frac{\kappa}{2}(\ln(J))^2$$

where $Q$ is a weighted sum of squared strain tensor components, scaled via a regional anisotropy parameter $\alpha$ (Baptiste et al., 21 Oct 2025). The model solves the finite-element equations to compute LA deformation under physiologic pressure boundary conditions, focusing on the reservoir phase and validating regional displacement against CT-derived feature tracking within ±0.90mm.

Electrophysiological activation patterns are recapitulated via reaction-diffusion models—often based on Mitchell–Schaeffer dynamics—on a high-resolution mesh (average 2.47mm voxel size). Local conduction velocities ($CV = 1 / \|\nabla LAT\|$) and activation times are tuned to best fit the patient’s electroanatomical mapping data (≥1,500 LAT recordings per patient) using optimization strategies (e.g., minimizing mean absolute LAT error to 5–11ms with correlation coefficients 0.81–0.95 across rhythms) (He et al., 2022, Solis-Lemus et al., 2023).

For models targeting the impact of interventions (ablation, pacing, antiarrhythmic pharmacology), the hybrid latent force–Gaussian process approach is relevant. Latent forces model temporally marked interventions with causal kernels, guaranteeing physical causality (zero effect prior to application), while patient-specific parameters allow inference of inter-individual variation in response. Analytical cross-covariance expressions enable scalable gradient-based inference on large, irregularly sampled datasets (Cheng et al., 2019).

4. Calibration, Validation, and Regional Heterogeneity

Patient-specific LA models are calibrated against subject image data by fitting regional material parameters (e.g., $\alpha$ in Guccione’s law) to match peak deformation and transient patterns throughout the cardiac cycle. Surrogate Gaussian process emulators facilitate high-dimensional parameter exploration. The history matching technique identifies non-implausible parameter regions via an implausibility index,

$$I^2(x) = \max_{i=1,\dots,m}\frac{(\mathbb{E}[f_i(x)]-\mu_i)^2}{\operatorname{Var}[f_i(x)]+\sigma_i^2}$$

and MCMC is used to characterize posterior distributions over the physiological input parameters (Baptiste et al., 21 Oct 2025). Validation of the mechanistic model is performed via image-derived regional displacement errors (mean ≈ 0.90 ± 0.39mm).

Empirical analyses consistently demonstrate that regional heterogeneity in myocardial stiffness ($\alpha$) is a far more significant determinant of LA deformation than anatomical features such as wall thickness or epicardial adipose tissue volume, which show minimal correlation with chamber biomechanics. Fitting spatially resolved stiffness enables realistic simulation of regional motion and may elucidate the influence of pathologic processes such as fibrosis on arrhythmic propensity.

5. Applications, Simulation Platforms, and Clinical Significance

Patient-specific LA models have demonstrated utility in several domains:

• Quantitative evaluation and iterative refinement of ablation strategies—by localizing triggers, mapping reentrant circuits, and personalizing ablation targets based on high-fidelity simulation of activation (He et al., 2022).
• Assessment of fibrotic burden, with robust reproducibility metrics in segmentation and mapping (Solis-Lemus et al., 2023, Mehrnia et al., 8 Nov 2024).
• Physical simulation platforms for pediatric valve repair planning, where 3D printed and silicone-molded valves are dynamically tested under simulated physiologic loading; metrics such as regurgitant orifice area, billow/tenting height/volume, and pressure gradients demonstrate repair efficacy and mechanical durability (Ching et al., 12 Aug 2025).
• Prediction of arrhythmia risk and tailored intervention design, enabling clinicians to anticipate arrhythmogenic remodeling and guide therapy to minimize recurrence and unnecessary tissue destruction (He et al., 2022).

6. Challenges, Limitations, and Future Advancements

Although model fidelity and reproducibility have improved, several obstacles remain. Operator-dependent variability in landmark selection continues to introduce uncertainty in segmentation, UAC mapping, and fiber orientation assignment (Solis-Lemus et al., 2023). Modest reproducibility in complex outputs (e.g., phase singularity maps, SSIM ~0.648 inter-operator) limits utility in quantitative arrhythmia substrate characterization.

Physical valve simulation platforms demonstrate high consistency in annular geometry but increased variability in dynamic leaflet closure metrics (e.g., tenting volume CV ≈ 73.5%), implicating silicone thickness and chordal tension heterogeneity as contributors (Ching et al., 12 Aug 2025). Model enhancements should address active myocardial motion, sophisticated chordal structures, and tissue-mimicking materials.

In computational frameworks, ongoing work emphasizes better integration of multi-modal imaging (CT, MRI, electroanatomical mapping), coupled biomechanical–electrophysiological simulation, and scalable parameter inference. Future studies may also incorporate error covariance modeling, longitudinal imaging to track disease progression, and real-time “what-if” scenario exploration in clinical decision support systems.

7. Cross-Modal Translation and Emerging Techniques

Recent work in related domains, such as patient-specific lung modeling or arterial network simulation, informs LA modeling through similar strategies—direct mapping of anatomy via imaging, reduced-order modeling, and individual-specific parameter inference using combined local/global sensitivity analysis and multi-start gradient-based optimization (Taylor-LaPole et al., 26 Jun 2024, Geitner et al., 2022, Rixner et al., 26 Aug 2024). Deep learning tools, such as foundational Med-SAM architectures, now enable rapid and accurate anatomical segmentation for constructing computational meshes, with robust performance across centers and imaging modalities (Mehrnia et al., 8 Nov 2024). For dynamic modeling, autoregressive transformer-based pipelines employing tokenized image sequences (e.g., via VQGAN+nnUNet) open the door to personalized motion prediction and adaptive simulation (Lai et al., 17 May 2025).

A plausible implication is that as these technologies mature, patient-specific LA model generation, calibration, and simulation will further integrate zero-shot segmentation, multi-modal data fusion, dynamic parameter inference, and direct clinical translation—significantly advancing understanding and treatment of arrhythmogenic cardiac pathophysiology.