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.
CITATION STYLE
Luo, G., Kuang, M., & Cao, P. (2023). Generalized Deep Learning-Based Proximal Gradient Descent for MR Reconstruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13897 LNAI, pp. 239–244). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34344-5_28
Mendeley helps you to discover research relevant for your work.