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) T₁ mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T₁ data from 61 patients by discarding the additional T₁-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T₁ mapping data in 27 subjects with inline T₁ map reconstruction by MyoMapNet. The resulting T₁ values were compared to MOLLI.

Results

MyoMapNet trained using a combination of native and post-contrast T₁-weighted images had excellent native and post-contrast T₁ accuracy compared to MOLLI. The FCNN model using four T₁-weighted images yields similar performance compared to five T₁-weighted images, suggesting that four T₁ weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T₁ maps on the scanner. Native and post-contrast myocardium T₁ by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T₁ was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively.

Conclusion

A FCNN, trained using MOLLI data, can estimate T₁ values from only four T₁-weighted images. MyoMapNet enables myocardial T₁ mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.