Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction

• URL: http://arxiv.org/abs/2211.16881v2
• Date: Sat, 18 Mar 2023 16:34:01 GMT
• Title: Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction
• Authors: Guanxiong Luo, Mengmeng Kuang, Peng Cao
• Abstract summary: The data consistency for the physical forward model is crucial in inverse problems, especially in MR imaging reconstruction. The deep learning-based proximal gradient descent was proposed and use a network as regularization term that is independent of the forward model. 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.
• Score: 3.128676265663467
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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