Quantification of myocardial oxygen extraction fraction on noncontrast MRI enabled by deep learning.

ABSTRACT:

Purpose

To develop a new deep learning enabled cardiovascular magnetic resonance (CMR) approach for noncontrast quantification of myocardial oxygen extraction fraction (mOEF) and myocardial blood volume (MBV) in vivo.

Materials and methods

An asymmetric spin-echo prepared CMR sequence was created in a 3 T MRI clinical system. A UNet-based fully connected neural network was developed based on a theoretical model of CMR signals to calculate mOEF and MBV. Twenty healthy volunteers (20-30 years old, 11 females) underwent CMR scans at 3 short-axial slices (16 myocardial segments) on 2 different days. The reproducibility was assessed by the coefficient of variation. Ten patients with chronic myocardial infarction were examined to evaluate the feasibility of this CMR method to dete

SUBMITTER: Li R 

PROVIDER: S-EPMC12245170 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature