Abstract #3621

Reconstructing non-Cartesian acquisitions using dAUTOMAP

Maarten L Terpstra^1,2, Federico d'Agata^1,2,3, Bjorn Stemkens^1,2, Jan JW Lagendijk¹, Cornelis AT van den Berg^1,2, and Rob HN Tijssen^1,2

¹Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, Netherlands, ²Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ³Department of Neurosciences, University of Turin, Turin, Italy

Recently, dAUTOMAP has been presented to perform deep learning-based image reconstruction. dAUTOMAP uses gridded k-space points and so far it has only been used to reconstruct Cartesian acquisitions. In this work, we demonstrate that dAUTOMAP can produce high-quality reconstructions on radial and spiral non-Cartesian acquisitions and can resolve artifacts beyond those introduced by the undersampled acquisition.

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