Magnetic Resonance Imaging-Based Coronary Flow: The Role of Artificial Intelligence

Abstract

Machine learning (ML) and deep learning (DL) techniques have been increasingly applied to help diagnose coronary artery disease (CAD) as well as help with patient management decisions. Imaging has begun to play a larger role in these studies. Cardiovascular magnetic resonance (CMR) offers multiple techniques to diagnose CAD, and ML and DL have been used with these techniques in an effort to improve both the image quality and the speed of image interpretation. In particular, ML and DL have been applied to direct imaging of coronary vessel anatomy, imaging of coronary flow, and myocardial perfusion imaging. In applications aimed at imaging the coronary artery anatomy, ML and DL techniques have been used to improve image quality in reconstruction, improve the speed of reconstruction, allow for more sparse sampling of data, and enable automated evaluation of image quality. In applications of coronary flow imaging, ML and DL techniques have been used to reduce the uncertainty of phase-contrast measurements of blood velocity and flow, and physics-informed neural networks have been used to improve the modeling of flow based on both acquired image data and natural laws of motion. In myocardial perfusion imaging, ML and DL techniques have been used at multiple steps in the image analysis process to automate quantitative blood flow measurements, including motion correction, image registration, tracer kinetic modeling, and detection of perfusion defects. Future applications of ML and DL in evaluating CAD are expected to continue to develop with increasing impact in both diagnosis and patient management.KeywordsMagnetic resonanceCoronary arteriesCoronary artery diseaseCoronary flowMachine learningDeep learning