Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
Motivation: Partial Fourier deep learning for under-sampled k-space reconstruction has not been well studied, it is worth investigating to combine the two methods to achieve better parallel imaging.
Goal(s): To achieve better partial Fourier reconstruction empowered by deep learning.
Approach: A deep learning reconstruction network with implicit phase constraint for partial Fourier imaging was proposed.
Results: The proposed method achieves better IQ in multi-anatomy compared to conventional partial Fourier reconstruction method.
Impact: Our method integrates implicit phase constraint into partial Fourier deep learning reconstruction network, it has been proved that the method works well in multi-anatomy, and it is expected the applications of partial Fourier with deep learning reconstruction can be expanded.
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