Enhancing patient-specific computational modeling of cardiac mechanics by leveraging high spatiotemporal resolution magnetic resonance imaging

BACKGROUND AND OBJECTIVE: Computational models of cardiac mechanics can improve diagnosis, surgical planning, and device design; yet their accuracy is limited by the quality of the underlying imaging data used in routine care. Our team recently reported a free-breathing, four-dimensional cardiac magnetic resonance imaging (MRI) protocol (AutoCMR), which provides 30 volumetric frames per cardiac cycle at 1.6mm isotropic resolution. Here, we investigate whether this high spatiotemporal resolution imaging data, along with cuff systolic and diastolic blood pressures (SBP, DBP), improves non-invasive calibration and internal consistency of patient-specific 0D lumped-parameter and 3D finite element (FE) cardiac models. METHODS: Five adults were imaged with the AutoCMR protocol. Every frame was segmented to obtain left ventricular (LV) and left atrial (LA) volume-time curves. These curves and SBP/DBP calibrated a closed-loop 0D model of LA, LV, and the systemic circulation. To assess the impact on key metrics, the LV elastance up-stroke exponent was varied, while all other calibrated parameters remained fixed. The resulting pressure-volume metrics included end-systolic elastance, effective arterial elastance, ventriculo-arterial coupling, stroke volume, ejection fraction, and stroke work. The calibrated parameters initialized a subject-specific 3D FE LV model. RESULTS: =0.973 for 3D. Absolute LV peak-to-cuff systolic pressure target differences averaged 2.81±1.91mmHg (0D) and 4.81±1.10mmHg (3D), while absolute arterial diastolic pressure target differences were 1.64±1.33mmHg (0D) and 1.58±1.51mmHg (3D). In baseline-referenced sensitivity analysis, the 30-frame cine constraints kept mid-systolic LV pressure deviations ∼3-4 mmHg, compared to ∼18-25 mmHg when only end-diastolic and end-systolic constraints were used. CONCLUSIONS: Thirty-frame 3D cine MRI at 1.6mm isotropic resolution, combined with SBP/DBP, supported non-invasive calibration of 0D lumped parameter and 3D FE models with close in-sample agreement to MRI-derived volumes and cuff-pressure targets. The achieved in-sample agreement indicates that this AutoCMR-based simulation approach has the potential to support patient-specific computational assessments of cardiac function in clinical settings.