TY - GEN
T1 - Ultra-fast T2-weighted MR reconstruction using complementary T1-weighted information
AU - Xiang, Lei
AU - Chen, Yong
AU - Chang, Weitang
AU - Zhan, Yiqiang
AU - Lin, Weili
AU - Wang, Qian
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired Magnetic Resonance Imaging (MRI) protocols that provide complementary information for diagnosis. However, the total acquisition time of ~10 min yields the image quality vulnerable to artifacts such as motion. To speed up MRI process, various algorithms have been proposed to reconstruct high quality images from under-sampled k-space data. These algorithms only employ the information of an individual protocol (e.g., T2WI). In this paper, we propose to combine complementary MRI protocols (i.e., T1WI and under-sampled T2WI particularly) to reconstruct the high-quality image (i.e., fully-sampled T2WI). To the best of our knowledge, this is the first work to utilize data from different MRI protocols to speed up the reconstruction of a target sequence. Specifically, we present a novel deep learning approach, namely Dense-Unet, to accomplish the reconstruction task. The Dense-Unet requires fewer parameters and less computation, but achieves better performance. Our results have shown that Dense-Unet can reconstruct a 3D T2WI volume in less than 10 s, i.e., with the acceleration rate as high as 8 or more but with negligible aliasing artefacts and signal-noise-ratio (SNR) loss.
AB - T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired Magnetic Resonance Imaging (MRI) protocols that provide complementary information for diagnosis. However, the total acquisition time of ~10 min yields the image quality vulnerable to artifacts such as motion. To speed up MRI process, various algorithms have been proposed to reconstruct high quality images from under-sampled k-space data. These algorithms only employ the information of an individual protocol (e.g., T2WI). In this paper, we propose to combine complementary MRI protocols (i.e., T1WI and under-sampled T2WI particularly) to reconstruct the high-quality image (i.e., fully-sampled T2WI). To the best of our knowledge, this is the first work to utilize data from different MRI protocols to speed up the reconstruction of a target sequence. Specifically, we present a novel deep learning approach, namely Dense-Unet, to accomplish the reconstruction task. The Dense-Unet requires fewer parameters and less computation, but achieves better performance. Our results have shown that Dense-Unet can reconstruct a 3D T2WI volume in less than 10 s, i.e., with the acceleration rate as high as 8 or more but with negligible aliasing artefacts and signal-noise-ratio (SNR) loss.
UR - http://www.scopus.com/inward/record.url?scp=85054102153&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00928-1_25
DO - 10.1007/978-3-030-00928-1_25
M3 - Conference contribution
AN - SCOPUS:85054102153
SN - 9783030009274
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 215
EP - 223
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 -