Automatic calculation of myocardial perfusion reserve using deep learning with uncertainty quantification.

ABSTRACT:

Background

Myocardial perfusion reserve index (MPRI) in magnetic resonance imaging (MRI) is an important indicator of ischemia, and its measurement typically involves manual procedures. The purposes of this study were to develop a fully automatic method for estimating the MPRI and to evaluate its performance.

Methods

The method consisted of segmenting the myocardium in dynamic contrast-enhanced (DCE) myocardial perfusion MRI data using Monte Carlo dropout U-Net, dividing the myocardium into segments based on landmark localization with machine learning, and estimating the MPRI after the calculation of the left ventricular and myocardial contrast upslopes. The proposed method was compared with a reference method, which involved manual adjustments of the myocardial contours and

SUBMITTER: Kim YC 

PROVIDER: S-EPMC10722070 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature