Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging.

ABSTRACT:

Purpose

We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow.

Methods

A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow.

Results

For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27°. For pixels with a veloci

SUBMITTER: Sun X 

PROVIDER: S-EPMC10160163 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature