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• W4205753121 abstract "Abstract Purpose To develop and evaluate MyoMapNet, a rapid myocardial T 1 mapping approach that uses fully connected neural networks (FCNN) to estimate T 1 values from four T 1 -weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4). Method We implemented an FCNN for MyoMapNet to estimate T 1 values from a reduced number of T 1 -weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T 1 , or a combination of both. We also explored the effects of number of T 1 -weighted images (four and five) for native T 1 . After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T 1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T 1 data from 61 patients by discarding the additional T 1 -weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T 1 mapping data in 27 subjects with inline T 1 map reconstruction by MyoMapNet. The resulting T 1 values were compared to MOLLI. Results MyoMapNet trained using a combination of native and post-contrast T 1 -weighted images had excellent native and post-contrast T 1 accuracy compared to MOLLI. The FCNN model using four T 1 -weighted images yields similar performance compared to five T 1 -weighted images, suggesting that four T 1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T 1 maps on the scanner. Native and post-contrast myocardium T 1 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 T 1 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 T 1 values from only four T 1 -weighted images. MyoMapNet enables myocardial T 1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction." @default.
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• W4205753121 date "2022-01-06" @default.
• W4205753121 modified "2023-10-15" @default.
• W4205753121 title "Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach" @default.
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• W4205753121 doi "https://doi.org/10.1186/s12968-021-00834-0" @default.
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