Objective assessment of cardiac function using patient-specific biophysical modeling based on cardiovascular MRI combined with catheterization

Synthesizing multimodality data, such as cardiovascular magnetic resonance imaging (MRI) combined with catheterization, into a single framework is challenging. Different acquisition systems are subjected to different measurement errors. Coupling clinical data with biomechanical models can assist in clinical data processing (e.g., model-based filtering of measurement noise) and quantify myocardial mechanics via metrics not readily available in the data, such as myocardial contractility. In this work, we use a biomechanical modeling with the aim 1) to quantitatively compare model- and data-derived signals, and 2) toexplore the potential of model-derived myocardial contractility and distal resistance of the circulation (R_d) to robustly quantify cardiovascular physiology. We used 51 ventricular catheterization pressure and cine MRI volume data sets from patients with single-ventricle physiology and left and right ventricles of patients with repaired tetralogy of Fallot. Ventricular time-varying elastance (TVE) metrics and linear regression were used to quantify the relationship between the maximum value of TVE (E_max) and maximum time derivative of ventricular pressure [max(dP/dt)] in data- and model-derived pressure and volume signals at P < 0.05. Pearson’s correlations were used to compare model-derived contractility and data-derived Emax and max(dP/dt), and model-derived Rd and data-derived vascular resistance. All data and model-derived linear regressions were significant (P < 0.05). Model-derived max(dP/dt) versus data-derived E_max produced a higher coefficient of determination of linear regression models (R²) than data derived max(dP/dt) versus data-derived E_max. Correlations demonstrated significant relationships between most data- and model derived metrics. This work revealed the clinical value of biomechanical modeling to assist in clinical data processing by providing high-quality pressure and volume signals and to quantify cardiovascular pathophysiology. NEW & NOTEWORTHY Combining cardiovascular MRI and catheterization data into a single framework provides important quantities of cardiovascular physiology. However, multimodality data are often subjected to measurement errors (e.g., time-dys synchrony between volume and pressure signals) limiting their clinical interpretation. This study demonstrates that coupling clinical data with biomechanical modeling is an efficient way to filter measurement noise (providing, e.g., high-quality pressure volume loops) and derive meaningful quantities of cardiovascular physiology.