Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning.

ABSTRACT:

Purpose

To develop a deep neural network-based computational workflow for inline myocardial perfusion analysis that automatically delineates the myocardium, which improves the clinical workflow and offers a "one-click" solution.

Materials and methods

In this retrospective study, consecutive adenosine stress and rest perfusion scans were acquired from three hospitals between October 1, 2018 and February 27, 2019. The training and validation set included 1825 perfusion series from 1034 patients (mean age, 60.6 years ± 14.2 [standard deviation]). The independent test set included 200 scans from 105 patients (mean age, 59.1 years ± 12.5). A convolutional neural network (CNN) model was trained to segment the left ventricular cavity, myocardium, and right ventricle by processing a

SUBMITTER: Xue H 

PROVIDER: S-EPMC7706884 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature