Original paper
Partial Fourier reconstruction of complex MR images using complex‐valued convolutional neural networks
Abstract
To provide a complex-valued deep learning approach for partial Fourier (PF) reconstruction of complex MR images.Conventional PF reconstruction methods, such as projection onto convex sets (POCS), uses low-resolution image phase information from the central symmetrically sampled k-space for image reconstruction. However, this smooth phase constraint undermines the phase estimation accuracy in presence of rapid local phase variations, causing...
Paper Details
Title
Partial Fourier reconstruction of complex MR images using complex‐valued convolutional neural networks
Published Date
Oct 5, 2021
Volume
87
Issue
2
Pages
999 - 1014
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History