Estimating right heart catheterization results from Phase-Resolved Functional Lung (PREFUL) MRI using Deep Learning

Motivation: Right heart catheterization (RHC) is the invasive gold standard to measure several cardiopulmonary parameters. PREFUL is a non-invasive method to quantify hemodynamics during the cardiac cycle from MRI in free breathing and without contrast agents. Goal(s): Provide an MRI-based method for non-invasive estimation of mean pulmonary arterial pressure (mPAP), pulmonary arterial wedge pressure (PAWP) and mixed venous oxygen saturation (SvO2) currently determined by RHC. Approach: Multiple deep neural networks were trained to estimate mPAP, PAWP and SvO2 from cardiac cycles of three lung slices, provided by PREFUL. Results: The estimations of mPAP, PAWP and SvO2 showed strong correlation with RHC. Impact: Estimation of mPAP, PAWP and SvO2 is possible via PREFUL MRI. The presented approach is more automatized than echocardiography and may support the diagnosis of cardiopulmonary diseases such as pulmonary hypertension where RHC is not yet available.