Compressed-Sensing Magnetic Resonance Image Reconstruction Using an Iterative Convolutional Neural Network Approach  
[PERSON]  
1 Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka 434-8601, Japan; [EMAIL_ADDRESS];jp (K.O.); [EMAIL_ADDRESS] (T.O.)1 Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka 434-8601, Japan; [EMAIL_ADDRESS];jp (K.O.); [EMAIL_ADDRESS] (T.O.)1 Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka 434-8601, Japan; [EMAIL_ADDRESS];jp (K.O.); [EMAIL_ADDRESS] (T.O.)1 Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka 434-8601, Japan; [EMAIL_ADDRESS];jp (K.O.); [EMAIL_ADDRESS] (T.O.)1 Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka 434-8601, Japan; [EMAIL_ADDRESS];jp (K.O.)1 Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka 434-8601, Japan; [EMAIL_ADDRESS];jp (K.O.)1 Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka 434-8601, Japan; [EMAIL_ADDRESS];jp (T.  
in reconstructed images. Compressed sensing [7] can be employed for MRI reconstruction [8; 9; 10; 11; 12] using techniques that utilize sparsity in specific transform domains, such as the wavelet [8; 10] and curvelet [11] transforms and dictionary learning [12], all of which have been incorporated in recently developed MRI scanners.  
Even though the development of improved image-processing methods is competitive, the use of convolutional neural networks (CNNs) to enhance the quality of medical images has increasingly attracted researchers' attention [13; 14; 15; 16; 17]. With respect to compressed-sensing MRI (CS-MRI) reconstruction, several extant studies have reported the possibility of obtaining high-quality images from undersampled data using CNNs [18; 19; 20; 21; 22; 23; 24] by training them to map undersampled images onto corresponding full-sampled images [18; 19]. Alternatively, in a few studies, a hybrid approach that operates on both the k-space and image space has been introduced to enhance image quality [22; 23]. For example, [PERSON] et al. reported on the first trial of a CNN-based CS-MRI approach that can restore the brain structures from zero-filling MR images [18]. [PERSON] et al. proposed a generative adversarial network (GAN)-based CS-MRI algorithm; however, it is difficult to train GANs stably [21]. In addition, [PERSON] et al. proposed KIKI-net, in which CNNs operate on both the k-space and image space [22]. This approach separately minimizes the loss functions in both spaces and improves image quality by restoring tissue structures and eliminating aliasing artifacts. [PERSON] et al. proposed a hybrid approach that employs CNNs and k-space correction, wherein the unfilled parts of k-space data are replaced by original k-space data [23]. This approach outperforms image-based CNNs; however, aliasing artifacts still remain due to the hybrid approach. Therefore, a more effective suppression of the aliasing artifacts is greatly needed.  
Our work, which was inspired by the above-mentioned research studies, involves the development of a novel iterative CNN-based method for CS-MRI reconstruction, which is presented in this paper. The method combines the operation of image-based CNNs with k-space corrections, and it demonstrates superior performance compared to standalone image-based CNN and noniterative k-space correction methods because the aliasing artifacts can be suppressed by the iterative processing of the proposed CS-MRI reconstruction. In this study, experiments were performed to analyze the quality of T2-weighted brain images that was realized using the proposed method, and image quality is expressed in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).