Partial Fourier reconstruction of complex MR images using complex‐valued convolutional neural networks

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...