Referenceless 4D flow cardiovascular magnetic resonance with deep learning.

ABSTRACT:

Background

Despite its potential to improve the assessment of cardiovascular diseases, four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) is hampered by long scan times. 4D flow CMR is conventionally acquired with three motion encodings and one reference encoding, as the three-dimensional velocity data are obtained by subtracting the phase of the reference from the phase of the motion encodings. In this study, we aim to use deep learning to predict the reference encoding from the three motion encodings for cardiovascular 4D flow.

Methods

A U-Net was trained with adversarial learning (U-Net_ADV) and with a velocity frequency-weighted loss function (U-Net_VEL) to predict the reference encoding from the three motion encodings obtained with a

SUBMITTER: Trenti C 

PROVIDER: S-EPMC12270065 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature