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 3T 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 create accelerated cardiac T1 mapping data.

Salah Assana

Research Assistant ll

I am a Artificial Intelligence and Healthcare enthusiast with a background in machine learning, signal processing and medical imaging.