“…The number of layers linearly increases in CD-CNNs with the number CD-CNN iterations. For example, the KIKInet used in this study, which combines 4 25-layer CNNs, corresponds to a CNN that consists of 100 layers (25 layers 3 4); however, if this extremely deep KIKI-net is trained in an end-to-end manner, there are several factors that degrade the training performance, such as the difficulty of hyperparameter tuning, [38][39][40] the vanishing gradient problem 41,42 due to a larger number of parameters, and graphic processing unit memory shortage. Furthermore, FT and IFT must be included in the networks, which increases the computational complexity of the network and worsen these F IGUR E 6 Reconstruction results from conventional algorithms and KIKI-net at R 5 4 undersampling for the T 2 fluid-attenuated inversion recovery Alzheimer's Disease Neuroimaging Initiative data set (T 2 -FLAIR_-ADNI): fully sampled image (A1); zero-filling image (B1); image reconstructed with compressed-sensing MRI (CS-MRI) (C1); image reconstructed with dictionary learning (DL) MRI (D1); image reconstructed with block-matching and 3D filtering (BM3D) MRI (E1); image reconstructed with Wang's algorithm (F1); image reconstructed with PANO (patch-based nonlocal operator) (G1); image reconstructed with FDLCP (fast dictionary learning method on classified patches) (H1); and image reconstructed with our proposed algorithm, KIKI-net (I1).…”