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- W4308716382 abstract "Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2D convolution since many magnetic resonance images or their corresponding k-space are in 2D. In this work, we present a new approach that explores the 1D convolution, making the deep network much easier to be trained and generalized. We further integrate the 1D convolution into the proposed deep network, named as One-dimensional Deep Low-rank and Sparse network (ODLS), which unrolls the iteration procedure of a low-rank and sparse reconstruction model. Extensive results on in vivo knee and brain datasets demonstrate that, the proposed ODLS is very suitable for the case of limited training subjects and provides improved reconstruction performance than state-of-the-art methods both visually and quantitatively. Additionally, ODLS also shows nice robustness to different undersampling scenarios and some mismatches between the training and test data. In summary, our work demonstrates that the 1D deep learning scheme is memory-efficient and robust in fast MRI." @default.
- W4308716382 created "2022-11-14" @default.
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- W4308716382 date "2021-12-09" @default.
- W4308716382 modified "2023-09-26" @default.
- W4308716382 title "One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI" @default.
- W4308716382 doi "https://doi.org/10.48550/arxiv.2112.04721" @default.
- W4308716382 hasPublicationYear "2021" @default.
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