$\textbf{MyoMapNet}$: Accelerated Modified Look-Locker Inversion Recovery Myocardial T1 Mapping via Neural Networks

TL;DR

MyoMapNet is a neural network-based method that rapidly estimates myocardial T1 and ECV maps from T1-weighted images, achieving similar accuracy to conventional methods but in a fraction of the time, within 4-5 heartbeats.

Contribution

This paper introduces MyoMapNet, a novel neural network approach for fast myocardial T1 mapping that reduces acquisition and processing time compared to traditional MOLLI sequences.

Findings

MyoMapNet's T1 estimates are comparable to MOLLI.

MyoMapNet significantly reduces estimation error compared to abbreviated-MOLLI.

Estimation time is approximately 2 ms per slice.

Abstract

Purpose: To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses neural networks (NN) to estimate voxel-wise myocardial T1 and extracellular (ECV) from T1-weighted images collected after a single inversion pulse over 4-5 heartbeats. Method: MyoMapNet utilizes a simple fully-connected NN to estimate T1 values from 5 (native) or 4 (post-contrast) T1-weighted images. Native MOLLI-5(3)3 T1 was collected in 717 subjects (386 males, 55$\pm$16.5 years) and post-contrast MOLLI-4(1)3(1)2 in 535 subjects (232 male, 56.5$\pm$15 years). The dataset was divided into training (80%) and testing (20%), where 20% of the training set was used to optimize MyoMapNet architecture (size and loss functions). We used MyoMapNet to estimate T1 and ECV maps with the first 5 (native) or 4 (post-contrast) T1-weighted images from the corresponding MOLLI sequence compared to the conventional and an abbreviated MOLLI using similar number of T1-weighted images with 3-parameter curve-fitting. Results: In our preliminary optimizaiton step, we determined that a 5-layers NN trained using mean-absolute-error loss yields lower estimation errors and was used subsequently in independent testing study. The myocardial T1 by MyoMapNet was similar to MOLLI (1200$\pm$45ms vs. 1199$\pm$46ms; P=0.3 for native T1, and 27.3$\pm$3.5% vs. 27.1$\pm$4%; P=0.4 for ECV). MyoMapNet had significantly smaller errors in T1 estimations compared to abbreviated-MOLLI (1$\pm$17ms vs. 31$\pm$34ms, P<0.01 for in native T1, and 0.1$\pm$1.3% vs. 1.9$\pm$2.5%, P<0.01 for ECV). The duration of T1 estimation was approximately 2 ms per slice using MyoMapNet. Conclusion: MyoMapNet T1 mapping enables myocardial T1 quantification in 4-5 heartbeats with near-instantaneous map estimation time with similar accuracy and precision as MOLLI. Keywords: Myocardial T1 mapping, MOLLI, T1 reconstruction, Neural network, Deep Learning.