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k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (MICCAI 2019)

Abstract

Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach for dynamic MR image reconstruction, termed k-t NEXT (k-t NEtwork with X-f Transform). In particular, inspired by traditional methods such as k-t BLAST and k-t FOCUSS, we propose to reconstruct the true signals from aliased signals in x-f domain to exploit the spatio-temporal redundancies. Building on that, the proposed method then learns to recover the signals by alternating the reconstruction process between the x-f space and image space in an iterative fashion. This enables the network to effectively capture useful information and jointly exploit spatio-temporal correlations from both complementary domains. Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.

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References

  1. Akçakaya, M., Moeller, S., Weingärtner, S., et al.: Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging. Magn. Reson. Med. 81(1), 439–453 (2019)

    Article  Google Scholar 

  2. Eo, T., Jun, Y., Kim, T., et al.: KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn. Reson. Med. 80(5), 2188–2201 (2018)

    Article  Google Scholar 

  3. Griswold, M.A., Jakob, P.M., Heidemann, R.M., et al.: Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47(6), 1202–1210 (2002)

    Article  Google Scholar 

  4. Hammernik, K., Klatzer, T., Kobler, E., et al.: Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med. 79(6), 3055–3071 (2018)

    Article  Google Scholar 

  5. Han, Y., Ye, J.C.: k-space deep learning for accelerated MRI. arXiv preprint arXiv:1805.03779 (2018)

  6. Hu, X., Parrish, T.: Reduction of field of view for dynamic imaging. Magn. Reson. Med. 31(6), 691–694 (1994)

    Article  Google Scholar 

  7. Jones, R., Haraldseth, O., Müller, T., et al.: K-space substitution: a novel dynamic imaging technique. Magn. Reson. Med. 29(6), 830–834 (1993)

    Article  Google Scholar 

  8. Jung, H., Sung, K., Nayak, K.S., et al.: k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. Magn. Reson. Med. 61(1), 103–116 (2009)

    Article  Google Scholar 

  9. Jung, H., Ye, J.C., Kim, E.Y.: Improved k-t BLAST and k-t SENSE using FOCUSS. Phys. Med. Biol. 52(11), 3201 (2007)

    Article  Google Scholar 

  10. Lingala, S.G., Hu, Y., DiBella, E., et al.: Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans. Med. Imaging 30(5), 1042–1054 (2011)

    Article  Google Scholar 

  11. Liu, J., Kuang, T., Zhang, X.: Image reconstruction by splitting deep learning regularization from iterative inversion. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 224–231. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_26

    Chapter  Google Scholar 

  12. Qin, C., Schlemper, J., Caballero, J., et al.: Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 38(1), 280–290 (2019)

    Article  Google Scholar 

  13. Schlemper, J., Caballero, J., Hajnal, J.V., et al.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37(2), 491–503 (2018)

    Article  Google Scholar 

  14. Schlemper, J., et al.: Bayesian deep learning for accelerated MR image reconstruction. In: Knoll, F., Maier, A., Rueckert, D. (eds.) MLMIR 2018. LNCS, vol. 11074, pp. 64–71. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00129-2_8

    Chapter  Google Scholar 

  15. Tsao, J., Boesiger, P., Pruessmann, K.P.: k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn. Reson. Med. 50(5), 1031–1042 (2003)

    Article  Google Scholar 

  16. Ye, J.C., Han, Y., Cha, E.: Deep convolutional framelets: a general deep learning framework for inverse problems. SIAM J. Imaging Sci. 11(2), 991–1048 (2018)

    Article  MathSciNet  Google Scholar 

  17. Zhang, P., Wang, F., Xu, W., et al.: Multi-channel generative adversarial network for parallel magnetic resonance image reconstruction in k-space. In: MICCAI, pp. 180–188 (2018)

    Google Scholar 

  18. Zhang, S., Block, K.T., Frahm, J.: Magnetic resonance imaging in real time: advances using radial FLASH. Magn. Reson. Med. 31(1), 101–109 (2010)

    Google Scholar 

Download references

Acknowledgement

This work was supported by EPSRC programme grant SmartHeart (EP/P001009/1).

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Correspondence to Chen Qin .

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Qin, C. et al. (2019). k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_56

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  • DOI: https://doi.org/10.1007/978-3-030-32245-8_56

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  • Online ISBN: 978-3-030-32245-8

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