Fig 1.
Overview of the modeling pipeline, from clinical data (input) to multi-scale, multi-physics cardiac models (output).
The framework components are described in detail in methods.
Table 1.
Clinical characteristics of the patient cohort with non-ischemic systolic HF.
Table 2.
ECG and cMRI findings of patient cohort with non-ischemic systolic HF.
Fig 2.
Automated estimation of the 3D anatomical model.
A) Automatic segmentation of the right and left ventricle. B) Observed variability in cardiac anatomy (shape is color-coded on a template) estimated from the HF cohort. The representation indicates the variability in phenotypes from the cohort. C) After the different steps of the model computation are finished, computed intracardiac volume variations can be estimated. D) Fiber architecture applied to the personalized heart models.
Fig 3.
Patient-specific electrophysiology computation.
Left Panels: Computed ECG traces from the model in the patient exemplarily chosen. Right Panels: Computed trans-membrane potential propagation throughout the cardiac cycle (time in % of cycle length).
Table 3.
Statistics of estimated Windkessel parameters throughout the studied population.
Fig 4.
Patient-specific hemodynamics.
A) Personalized computation of arterial flow after personalization of the Windkessel parameters. B) Measured and computed LV pressure and volume curve in one patient, showing the high concordance between the clinical and modeling data.
Table 4.
Correlations between LV active force and LV stiffness and clinical presentations of patients.
Fig 5.
Correlation between Left ventricular active force and estimated outcome of the patients.
Correlation plot showing the left ventricular active force in the patients (x-axis) and their Seattle 5 Year Score (y-axis).