## 1 Introduction  
Magnetic resonance imaging (MRI) has the advantage of being able to depict detailed brain structures non-invasively using a magnetic field; therefore, it is widely used clinically. The sensitivity of MRI has increased over time, from 1.5T systems to 3T or 7T systems, allowing high-resolution images to be obtained. The acquisition of high-resolution images from the same MRI system can increase the contrast-to-noise ratio (CNR), resulting in better tissue-to-tissue contrast. However, the signal-to-noise ratio (SNR) confirming the quality of the image is decreased, because of the reduced voxel size, making it difficult to clearly identify subtle brain structures [1; 2; 3; 4]. In addition, in order to increase the resolution of an image using MRI, more k-space phase-encoding lines need to be acquired, which not only increases the image acquisition time but also tends to lead to more motion artifacts [5; 6; 7]. Therefore, new image acquisition and reconstruction techniques for acquiring high-resolution images with high temporal resolution are being developed [8; 9; 10; 11; 12; 13; 14; 15].  
Various technologies are being developed to reduce acquisition time and motion artifacts in high-resolution imaging, including image averaging and compressed sensing (CS) techniques [16; 17; 18; 19]. Image averaging is a technique that reduces noise when different images are averaged [16; 17]. A high-resolution image has a relatively low SNR because of the high levels of noise; however, averaging several high-resolution images produces a high SNR due to the signal reduction resulting from averaging the random noises. However, to average several images, it is necessary to obtain a large number of high-resolution images in advance, and this further increases the acquisition time. The CS technique acquires randomly and reconstructs only some of the k-space phase-encoding lines, and even if undersampled, the obtained data can be reconstructed into an image of similar quality to the full k-space sampled image [18; 19]. While these CS techniques can reduce image acquisition time [20; 21], they have limitations in that the low SNR problem, which is a natural consequence of obtaining high-resolution images using 3T MRI, still needs to be addressed further.  
Recently, to overcome this limitation, the CS technique and the image averaging technique were combined to reduce image acquisition time and enable image acquisition with a high SNR value [22; 23]. In the phantom simulation, the CS averaging with keyhole acquisition (CSAK) method was proposed, which was an improvement on the existing multiple acquisition CSA (CSAM) method. The CSAM technique creates an image by averaging multiple images, undersampled using the CS technique. In contrast, the CSAK technique obtains all the undersampled high-frequency k-space dataset in the first subset, and the rest of the subsets contain only k-space center lines, which are low-frequency components that contain most of the image information. These keyhole-like techniques were used to increase temporal resolution. The incoherent high-frequency components of the first subset are shared pseudo-randomly with the rest of the subsets obtained using keyhole techniques and then reconstructed, and individual reconstructed images are averaged to increase the SNR. Using in vivo human experiments, CSAK based on the high-frequency sharing (i.e., CSAKS) technique was found to have higher temporal and spatial resolutions than those of CSAM; however, the limitations of ringing and/or blurring artifacts still remain to be solved for both techniques.  
To solve this problem, we propose a CSAK with high-frequency copying (CSAKC) technique that copies all high-frequency components of the undersampled first subset to the remaining subsets obtained only with keyhole data, and then performs the CS reconstruction procedure. In addition, we investigate the existing limitations by comparing it with other CSA techniques.