Reza Avazmohammadi

This paper introduces a physics-informed neural network to predict myocardial mechanics from physiological measurements, showing improved stress prediction and disease stratification in pulmonary hypertension.

This study integrates MRI modalities with computational fluid dynamics to model left ventricular flow, improving clinical assessment of cardiac hemodynamics without complex myocardial modeling.

This paper develops a super-resolution reconstruction framework to improve the quantification of complete spatiotemporal myocardial strains in mice using cardiac MRI.

This study uses a 1D-FSI model to quantify how vascular stiffening and resistance contribute to increased MPA pressure in pulmonary hypertension.

This paper develops a non-invasive ML model to identify infarcted myocardium using cardiac strain data, combining synthetic rodent data with limited human data to predict LGE ground truth.

This study investigates RV biomechanical remodeling in PH, finding that collagen fiber tautness and myofiber reorientation contribute to severe RV dysfunction and failure.

The paper develops a biophysical model of the lung to quantify pulmonary surfactant's effect on lung compliance by simulating respiration and surfactant dysfunction.

This paper introduces an in-silico heart phantom to validate cardiac strain imaging algorithms by simulating 4D cardiac motion and testing strain quantification accuracy.