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

^{1}Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, Netherlands, ^{2}Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ^{3}Department 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.

The dAUTOMAP model has been successfully demonstrated to reconstruct undersampled Cartesian acquisitions with high quality. Here we assess the performance of dAUTOMAP for non-Cartesian sampling strategies. We hypothesize that these acquisitions can be further undersampled than Cartesian acquisitions due to their benign undersampling artifacts. Reconstructing non-Cartesian acquisitions with dAUTOMAP requires that the k-space points are interpolated on a Cartesian grid because of the assumptions of the DT-layers. This creates additional artifacts because the gridding process operates with a finite-sized interpolation kernel. This introduces an undesired modulation in the reconstructed image, known as the roll-off effect

In this work, we show that dAUTOMAP is able to reconstruct gridded, non-Cartesian acquisitions with high quality while resolving artifacts introduced by gridding step and retaining favorable properties of the respective sampling strategies.

**Cartesian**: As a benchmark we included a Cartesian undersampling scheme. In this randomly undersampled acquisition, the 17 central lines of k-space were always sampled, complemented with random peripheral k-space lines to match the acceleration factor.**Radial**: Undersampled k-space was created with a golden-angle radial trajectory. The number of spokes was selected such that R=1 would be sampled at the Nyquist rate, requiring $$$224 \cdot \pi/2=352$$$ spokes. For factors 2, 4, and 8 we used 176, 88 and 44 spokes, respectively.**Spiral**: A variable-density spiral^{[4]}with 40 uniformly rotating interleaves was designed to be oversampled in the center of k-space and smoothly move to the edge of k-space. For R=2, 4, and 8 we used 20, 10, and 5 spirals, respectively.

Typical reconstructions are shown in

These results show that dAUTOMAP can reconstruct any trajectory after gridding and can even perform complex transforms inherent to the gridding operation such as roll-off correction.

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