Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
권호 표지
DOI QR코드

DOI QR Code

A Self-Supervised Learning Framework for Under-Sampling Pattern Design Using Graph Convolution Network

  • Li, Yuze (Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University) ;
  • Chen, Huijun (Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University)
  • Received : 2020.07.22
  • Accepted : 2020.09.12
  • Published : 2020.12.31
⟨ Previous Next ⟩

Abstract

Purpose: To generate the under-sampling pattern using a self-supervised learning framework based on a graph convolutional network. Materials and Methods: We first decoded the k-space data into the graph and put it into the network. After the processing of graph convolution layers and graph pooling layers, the network generated the under-sampling pattern for MR reconstruction. We trained the network on the simulated brain dataset enabled by the self-supervised learning strategy. We did simulation along with the in vivo brain and liver experiments under different noise levels and accelerating factors to compare the performance between the proposed method and traditional methods using the PSNR and SSIM index. Results: The simulation experiments showed that the proposed method can achieve the best performance with low accelerating factors (2 and 3) at all noise levels and in high accelerating factors (4 and 5) at high noise levels (50 and 70 dB). In in vivo experiments, the proposed method attained the highest PSNR and SSIM in the brain dataset as well as in the liver dataset after fine tuning on a small liver dataset. Conclusion: The self-supervised learning framework based on a graph convolutional network was able to design the under-sampling mask for MR reconstruction. The superior performance in the simulation and in vivo experiments demonstrated the feasibility and flexibility of the proposed method and its potential in clinical use.

Keywords

References

  1. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999;42:952-962 https://doi.org/10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S
  2. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002;47:1202-1210 https://doi.org/10.1002/mrm.10171
  3. Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58:1182-1195 https://doi.org/10.1002/mrm.21391
  4. Knoll F, Clason C, Diwoky C, Stollberger R. Adapted random sampling patterns for accelerated MRI. MAGMA 2011;24:43-50 https://doi.org/10.1007/s10334-010-0234-7
  5. Zhang Y, Peterson BS, Ji G, Dong Z. Energy preserved sampling for compressed sensing MRI. Comput Math Methods Med 2014;2014:546814 https://doi.org/10.1155/2014/546814
  6. Vellagoundar J, Machireddy RR. A robust adaptive sampling method for faster acquisition of MR images. Magn Reson Imaging 2015;33:635-643 https://doi.org/10.1016/j.mri.2015.01.008
  7. Seeger M, Nickisch H, Pohmann R, Scholkopf B. Optimization of k-space trajectories for compressed sensing by Bayesian experimental design. Magn Reson Med 2010;63:116-126 https://doi.org/10.1002/mrm.22180
  8. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60-88 https://doi.org/10.1016/j.media.2017.07.005
  9. Zhang Z, Cui P, Zhu W. Deep learning on graphs: a survey. arXiv, 2018;1812.04202
  10. Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. ICLR 2017
  11. Gao H, Ji S. Graph U-nets. ICML 2019
  12. Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. ICML 2016
  13. Goldstein T, Osher S. The split Bregman method for L1-regularized problems. Siam J Imaging Science 2009;2:323-343 https://doi.org/10.1137/080725891
  14. https://brainweb.bic.mni.mcgill.ca/brainweb/. Accessed November 22, 2019
  15. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004;13:600-612 https://doi.org/10.1109/TIP.2003.819861