Hyunseok Seo Kelly M. Shin Yeunwoong Kyung 2024-01-12T03:45:09Z 2024-01-12T03:45:09Z 2021-09-29 2021-09 1522-4880 https://pubs.kist.re.kr/handle/201004/77354 Fast magnetic resonance imaging (MRI) scan is usually achieved by undersampling in k-space, and reconstruction methods for image domain is indispensable. Conventional reconstruction methods rely on independent sensitivity maps of the receiver coils to synthesize the components of the spatial harmonics in k-space. In recent years, deep learningbased MRI algorithms have been providing more accurate reconstructed image than the traditional results. Nonetheless, there is room for improvement. In this study, we proposed a new deep learning-based reconstruction algorithm to use image and k-space domain data simultaneously. The Gabor filter was also defined to effectively incorporate two different domain data in learning stage. Experimental results using real MRI data showed that the proposed method outperforms other deep learning-based algorithms for three metrics of nMSE, SSIM, and VIF. English IEEE A DUAL DOMAIN NETWORK FOR MRI RECONSTRUCTION USING GABOR LOSS Conference 10.1109/ICIP42928.2021.9506197 1 IEEE International Conference on Image Processing (ICIP), pp.146 - 149 IEEE International Conference on Image Processing (ICIP) 146 149 US Anchorage, Alaska, USA 2021-09-19 2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) 000819455100030 2-s2.0-85125565391