Abstract #3791

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

Maximilian Zubke^1,2, Marius Wernz^1,2, Tawfik Moher Alsady^1,2, Till F Kaireit^1,2, Robin A Mueller^1,2, Andreas Voskrebenzev^1,2, Frank Wacker^1,2, Karen M Olsson^2,3, Marius M Hoeper^2,3, and Jens Vogel-Claussen^1,2

¹Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany, ²Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany, ³Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, Hannover, Germany

Synopsis

Keywords: Diagnosis/Prediction, Hypertension

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.

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