TY - GEN
T1 - A DUAL DOMAIN NETWORK FOR MRI RECONSTRUCTION USING GABOR LOSS
AU - Seo, Hyunseok
AU - Shin, Kelly M.
AU - Kyung, Yeunwoong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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 learning-based 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.
AB - 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 learning-based 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.
KW - Deep learning
KW - Gabor filter
KW - Image reconstruction
KW - MRI
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85125565391&partnerID=8YFLogxK
U2 - 10.1109/ICIP42928.2021.9506197
DO - 10.1109/ICIP42928.2021.9506197
M3 - Conference contribution
AN - SCOPUS:85125565391
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 146
EP - 149
BT - 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PB - IEEE Computer Society
T2 - 28th IEEE International Conference on Image Processing, ICIP 2021
Y2 - 19 September 2021 through 22 September 2021
ER -