KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images

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To demonstrate accurate MR image reconstruction from undersampled k‐space data using cross‐domain convolutional neural networks (CNNs)

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An overview of deep learning in medical imaging focusing on MRI (2018, neutral): “[141, 142, 143, 144], a fundamental field rapidly progressing.”
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks (2020, unknown): “KIKI-net [24] iteratively alternates between the image domain (I-CNN) and the k-space (K-CNN) where the data consistency constraint is enforced in an interleaving manner, as shown in Fig.”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, background): “Another approach was recently developed that combines k-space and image space CNN data processing [38].”
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021, methodology): “AIRS Medical: The AIRS Medical model used a combination of image- and k-space domain processing in a fashion analogous (but distinct) from that used in KIKI-Net [8].”