Impact of deep learning architectures on accelerated cardiac T₁ mapping using MyoMapNet.

ABSTRACT: The objective of the current study was to investigate the performance of various deep learning (DL) architectures for MyoMapNet, a DL model for T₁ estimation using accelerated cardiac T₁ mapping from four T₁ -weighted images collected after a single inversion pulse (Look-Locker 4 [LL4]). We implemented and tested three DL architectures for MyoMapNet: (a) a fully connected neural network (FC), (b) convolutional neural networks (VGG19, ResNet50), and (c) encoder-decoder networks with skip connections (ResUNet, U-Net). Modified Look-Locker inversion recovery (MOLLI) images from 749 patients at 3 T were used for training, validation, and testing. The first four T₁ -weighted images from MOLLI5(3)3 and/or MOLLI4(1)3(1)2 protocols were extracted to crea

SUBMITTER: Amyar A 

PROVIDER: S-EPMC9532368 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature