Abstract
PurposeTo 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).MethodWe 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.ResultsMyoMapNet 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.ConclusionA 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.
Highlights
Cardiovascular magnetic resonance (CMR) myocardialT1 and extracellular volume (ECV) mapping enable noninvasive quantification of diffuse interstitial fibrosis [1].Generally, myocardial T1 mapping consists of a preparation pulse and collection of a series of images to sampleGuo et al Journal of Cardiovascular Magnetic Resonance (2022) 24:6 the recovering longitudinal magnetization at different time points
MyoMapNet Look-Locker in 4 heartbeats (LL4) performs an inversion pulse followed by four ECGtriggered single-shot balanced steady-state free precession images acquired on successive cardiac cycles within a single breath-hold (Fig. 1A)
We found that MyoMapNet5, PreGd with 5 T1-weighted images did not significantly improve T1 precision compared to MyoMapNet4, PreGd or MyoMapNet4, Pre+PostGd with only 4 T 1-weighted images for native T1 or both native and post-contrast T 1
Summary
Cardiovascular magnetic resonance (CMR) myocardialT1 and extracellular volume (ECV) mapping enable noninvasive quantification of diffuse interstitial fibrosis [1].Generally, myocardial T1 mapping consists of a preparation pulse and collection of a series of images to sampleGuo et al Journal of Cardiovascular Magnetic Resonance (2022) 24:6 the recovering longitudinal magnetization at different time points. There is growing interest in using a single sequence to simultaneously measure different tissue relaxation times [16,17,18,19,20,21,22]. These approaches often require a more complicated fitting model with more parameters, resulting in a loss of precision and significantly longer reconstruction time, which reduce their clinical utility
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