Accelerated cardiac T₁ mapping in four heartbeats with inline MyoMapNet: a deep learning-based T₁ estimation approach.

ABSTRACT:

Purpose

To develop and evaluate MyoMapNet, a rapid myocardial T₁ mapping approach that uses fully connected neural networks (FCNN) to estimate T₁ values from four T₁-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4).

Method

We implemented an FCNN for MyoMapNet to estimate T₁ values from a reduced number of T₁-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T₁, or a combination of both. We also explored the effects of number of T₁-weighted images (four and five) for native T₁. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI)

SUBMITTER: Guo R 

PROVIDER: S-EPMC8734349 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature