- Keywords: MR Spectroscopy, Geometric Deep Learning, U-Net
- Abstract: Geometric deep learning has recently gained influence, as it allows the extension of convolutional neural networks to non euclidean domains. In this paper graph neural networks (GNNs) are used for reconstruction and coil combination of undersampled concentric-ring k-space MRSI data. We show that graph U-nets perform better on undersampled data than GNNs. Specifically, results suggest that the omission of self-connecting edges results in a more stable behavior and better training for graph U-nets.
- Paper Type: methodological development
- Primary Subject Area: Image Acquisition and Reconstruction
- Secondary Subject Area: Application: Radiology
- Paper Status: original work, not submitted yet
- Source Code Url: https://github.com/weiserjpaul/Graph-U-Net
- Data Set Url: We are very sorry, we had unforeseen technical difficulties. The data will be uploaded later on.
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