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Abstract #0241

Deep Subspace Learning for Improved T1 Mapping using Single-shot Inversion-Recovery Radial FLASH

Moritz Blumenthal1, Xiaoqing Wang1,2, and Martin Uecker1,2,3
1Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany, 2DZHK (German Centre for Cardiovascular Research), Göttingen, Germany, 3Institute of Medical Engineering, Graz University of Technology, Graz, Austria


Subspace based reconstructions can be used for quantitative imaging. We extend the recently added neural network framework in BART to support non-Cartesian trajectories and linear subspace constraint reconstructions. The network is trained to reconstruct coefficient maps from single-shot radial FLASH inversion recovery acquisitions. T1 maps are estimated using pixel-wise fitting to the signal model. The reconstruction quality of the coefficient and T1 maps are improved compared to an $$$\ell_1$$$-Wavelet regularized reconstruction. The subspace constraint network can be used for any linear subspace constraint reconstruction.

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