Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient
Dynamic MR Image Reconstruction
- URL: http://arxiv.org/abs/2205.01770v1
- Date: Tue, 3 May 2022 20:37:21 GMT
- Title: Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient
Dynamic MR Image Reconstruction
- Authors: Zihao Chen, Yuhua Chen, Yibin Xie, Debiao Li, Anthony G. Christodoulou
- Abstract summary: Data-consistent (DC) deep learning can accelerate reconstruction with good image quality, but has not been formulated for non-Cartesian subspace imaging.
We propose a DC non-Cartesian deep subspace learning framework for fast, accurate dynamic MR image reconstruction.
- Score: 17.713927354433398
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Non-Cartesian sampling with subspace-constrained image reconstruction is a
popular approach to dynamic MRI, but slow iterative reconstruction limits its
clinical application. Data-consistent (DC) deep learning can accelerate
reconstruction with good image quality, but has not been formulated for
non-Cartesian subspace imaging. In this study, we propose a DC non-Cartesian
deep subspace learning framework for fast, accurate dynamic MR image
reconstruction. Four novel DC formulations are developed and evaluated: two
gradient decent approaches, a directly solved approach, and a conjugate
gradient approach. We applied a U-Net model with and without DC layers to
reconstruct T1-weighted images for cardiac MR Multitasking (an advanced
multidimensional imaging method), comparing our results to the iteratively
reconstructed reference. Experimental results show that the proposed framework
significantly improves reconstruction accuracy over the U-Net model without DC,
while significantly accelerating the reconstruction over conventional iterative
reconstruction.
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