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arXiv · 2211.16881

Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction

Guanxiong Luo·Mengmeng Kuang·Peng Cao
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Abstract

The data consistency for the physical forward model is crucial in inverse problems, especially in MR imaging reconstruction. The standard way is to unroll an iterative algorithm into a neural network with a forward model embedded. The forward model always changes in clinical practice, so the learning component's entanglement with the forward model makes the reconstruction hard to generalize. The deep learning-based proximal gradient descent was proposed and use a network as regularization term that is independent of the forward model, which makes it more generalizable for different MR acquisition settings. This one-time pre-trained regularization is applied to different MR acquisition settings and was compared to conventional L1 regularization showing ~3 dB improvement in the peak signal-to-noise ratio. We also demonstrated the flexibility of the proposed method in choosing different undersampling patterns.

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Guanxiong Luo, Mengmeng Kuang, Peng Cao. 2023-03-18. Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction. https://doi.org/10.1007/978-3-031-34344-5_28

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