TY - GEN
T1 - Multi-channel generative adversarial network for parallel magnetic resonance image reconstruction in k-space
AU - Zhang, Pengyue
AU - Wang, Fusheng
AU - Xu, Wei
AU - Li, Yu
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Magnetic Resonance Imaging (MRI) typically collects data below the Nyquist sampling rate for imaging acceleration. To remove aliasing artifacts, we propose a multi-channel deep generative adversarial network (GAN) model for MRI reconstruction. Because multi-channel GAN matches the parallel data acquisition system architecture on a modern MRI scanner, this model can effectively learn intrinsic data correlation associated with MRI hardware from originally-collected multi-channel complex data. By estimating missing data directly with the trained network, images may be generated from undersampled multi-channel raw data, providing an “end-to-end” approach to parallel MRI reconstruction. By experimentally comparing with other methods, it is demonstrated that multi-channel GAN can perform image reconstruction with an affordable computation cost and an imaging acceleration factor higher than the current clinical standard.
AB - Magnetic Resonance Imaging (MRI) typically collects data below the Nyquist sampling rate for imaging acceleration. To remove aliasing artifacts, we propose a multi-channel deep generative adversarial network (GAN) model for MRI reconstruction. Because multi-channel GAN matches the parallel data acquisition system architecture on a modern MRI scanner, this model can effectively learn intrinsic data correlation associated with MRI hardware from originally-collected multi-channel complex data. By estimating missing data directly with the trained network, images may be generated from undersampled multi-channel raw data, providing an “end-to-end” approach to parallel MRI reconstruction. By experimentally comparing with other methods, it is demonstrated that multi-channel GAN can perform image reconstruction with an affordable computation cost and an imaging acceleration factor higher than the current clinical standard.
UR - https://www.scopus.com/pages/publications/85054061791
U2 - 10.1007/978-3-030-00928-1_21
DO - 10.1007/978-3-030-00928-1_21
M3 - Conference contribution
AN - SCOPUS:85054061791
SN - 9783030009274
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 180
EP - 188
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Schnabel, Julia A.
A2 - Davatzikos, Christos
A2 - Alberola-López, Carlos
A2 - Fichtinger, Gabor
A2 - Frangi, Alejandro F.
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -