Ultra-fast T2-weighted MR reconstruction using complementary T1-weighted information

Lei Xiang, Yong Chen, Weitang Chang, Yiqiang Zhan, Weili Lin, Qian Wang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsJulia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi
PublisherSpringer Verlag
Pages215-223
Number of pages9
ISBN (Print)9783030009274
DOIs
Publication statusPublished - 2018 Jan 1
Externally publishedYes
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 2018 Sep 162018 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11070 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period18/9/1618/9/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Xiang, L., Chen, Y., Chang, W., Zhan, Y., Lin, W., Wang, Q., & Shen, D. (2018). Ultra-fast T2-weighted MR reconstruction using complementary T1-weighted information. In J. A. Schnabel, C. Davatzikos, C. Alberola-López, G. Fichtinger, & A. F. Frangi (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 215-223). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11070 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00928-1_25