Abstract #0958

Deep learning reconstruction of radial T2 weighted data sets with data consistent unrolled neural networks

Brian Toner¹, Simon Arberet², Fei Han³, Mariappan Nadar², Vibhas Deshpande⁴, Diego Martin⁵, Maria Altbach⁶, and Ali Bilgin^7,8

¹Applied Mathematics, The University of Arizona, Tucson, AZ, United States, ²Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, United States, ³Siemens Healthineers, Los Angeles, CA, United States, ⁴Siemens Healthineers, Austin, TX, United States, ⁵Radiology, Houston Methodist, Houston, TX, United States, ⁶Medical Imaging, The University of Arizona, Tucson, AZ, United States, ⁷Electrical and Computer Engineering, The University of Arizona, Tucson, AZ, United States, ⁸Biomedical Engineering, The University of Arizona, Tucson, AZ, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionUnrolled networks with data consistency layers have been shown to be effective in reconstructing MRI data. Radial turbo spin echo sequences enable acquisition of multi-contrast k-space data, which can be used to generate multi-contrast images at different echo times together with a co-registered T2 map. In this work, we will show that the cascading unrolled network architecture is effective in reconstructing images from radial turbo spin echo data. In order to do this, data consistency layers must be implemented to be able to combine data from multi-coil acquisitions and from multiple echo times.

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