Reconstruction and coil combination of undersampled concentric-ring MRSI data using a Graph U-NetDownload PDF

10 Apr 2021, 06:47 (modified: 22 Jul 2022, 20:09)MIDL Short 2021 PosterReaders: Everyone
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
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