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Abstract #0782

A Joint Multi-Scale Variational Neural Network for Accelerating Free-breathing Whole-Heart qBOOST-T2 mapping

Niccolo Fuin1, Giorgia Milotta1, Thomas Kuestner1, Aurelien Bustin1, Gastao Cruz1, Rene Botnar1,2, and Claudia Prieto1,2
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Pontificia Universidad Católica de Chile, Santiago, Chile

T2 mapping is a promising technique for the characterization of myocardial inflammation and oedema. We recently proposed a quantitative 3D whole-heart sequence (qBOOST-T2) which provides co-registered 3D high-resolution bright-blood and T2 map volumes from a single free-breathing scan. However, high-resolution qBOOST-T2 requires long scan times of ~10 min. Here we propose a joint Multi-Scale Variational Neural Network (jMS-VNN) to enable the acquisition of 3D high-resolution bright-blood and accurate T2 map volumes in ~3 mins, and their reconstruction in ~30s. The proposed jMS-VNN jointly reconstructs data from multiple contrasts and efficiently apply dictionary-based signal matching for fast T2 map generation.

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