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

Synthesizing multi-modality 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) to explore 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 datasets 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 E max and max( dP/dT), and model-derived R d and data-derived vascular resistance. All data and model-derived linear regressions were significant (p<0.05). Model-derived max( dP/dT) vs. data-derived E max produced higher R 2 than data-derived max( dP/dT) vs. 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.