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Automated CNN-Based Reconstruction of Short-Axis Cardiac MR Sequence from Real-Time Image Data

  • Eric KerfootEmail author
  • Esther Puyol Anton
  • Bram Ruijsink
  • James Clough
  • Andrew P. King
  • Julia A. Schnabel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

We present a methodology for reconstructing full-cycle respiratory and cardiac gated short-axis cine MR sequences from real-time MR data. For patients who are too ill or otherwise incapable of consistent breath holds, real-time MR sequences are the preferred means of acquiring cardiac images, but suffer from inferior image quality compared to standard short-axis sequences and lack cardiac ECG gating. To construct a sequence from real-time images which, as close as possible, replicates the characteristics of short-axis series, the phase of the cardiac cycle must be estimated for each image and the left ventricle identified, to be used as a landmark for slice re-alignment. Our method employs CNN-based deep learning to segment the left ventricle in the real-time sequence, which is then used to estimate the pool volume and thus the position of each image in the cardiac cycle. We then use manifold learning to account for the respiratory cycle so as to select images of the best quality at expiration. From these images a selection is made to automatically reconstruct a single cardiac cycle, and the images and segmentations are then aligned. The aligned pool segmentations can then be used to calculate volume over time and thus volume-based biomarkers.

Keywords

Automatic segmentation Real time cardiac imaging Image-based motion correction 

Notes

Acknowledgements

This research was partly supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at Guy’s and St Thomas’ NHS Foundation Trust. Views expressed are those of the authors and not necessarily of the NHS, the NIHR, or the Dept. of Health.

References

  1. 1.
    van Amerom, J., et al.: Fetal cardiac cine imaging using highly accelerated dynamic MRI with retrospective motion correction and outlier rejection. Magn. Reson. Med. 79, 327–338 (2017)CrossRefGoogle Scholar
  2. 2.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)CrossRefGoogle Scholar
  3. 3.
    Cahalin, L.P., et al.: A meta-analysis of the prognostic significance of cardiopulmonary exercise testing in patients with heart failure. Heart Fail. Rev. 18(1), 79–94 (2013)CrossRefGoogle Scholar
  4. 4.
    Feng, L., Axel, L., Chandarana, H., Block, K.T., Sodickson, D.K., Otazo, R.: XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn. Reson. Med. 75(2), 775–788 (2016)CrossRefGoogle Scholar
  5. 5.
    Hansen, M., Sørensen, T., Arai, A., Kellman, P.: Retrospective reconstruction of high temporal resolution cine images from real-time MRI using iterative motion correction. Magn. Reson. Med. 68(3), 741–750 (2012)CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. CoRR abs/1502.01852 (2015)Google Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. CoRR abs/1603.05027 (2016)Google Scholar
  8. 8.
    Kerfoot, E., et al.: Eidolon: visualization and computational framework for multi-modal biomedical data analysis. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, S.-L. (eds.) MIAR 2016. LNCS, vol. 9805, pp. 425–437. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-43775-0_39CrossRefGoogle Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105. NIPS 2012, Curran Associates Inc., USA (2012)Google Scholar
  10. 10.
    La Gerche, A., et al.: Cardiac MRI: a new gold standard for ventricular volume quantification during high-intensity exercise. Circ. Cardiovasc. Imaging 6(2), 329–338 (2013)CrossRefGoogle Scholar
  11. 11.
    Lurz, P., et al.: Feasibility and reproducibility of biventricular volumetric assessment of cardiac function during exercise using real-time radial k-t SENSE magnetic resonance imaging. J. Magn. Reson. Imaging 29(5), 1062–1070 (2009)CrossRefGoogle Scholar
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015)Google Scholar
  13. 13.
    Ruijsink, B.: Semi-automatic cardiac and respiratory gated MRI for cardiac assessment during exercise. In: Cardoso, M.J. (ed.) CMMI/SWITCH/RAMBO - 2017. LNCS, vol. 10555, pp. 86–95. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67564-0_9CrossRefGoogle Scholar
  14. 14.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1. chap. Learning Internal Representations by Error Propagation, pp. 318–362. MIT Press, Cambridge (1986)Google Scholar
  15. 15.
    Simard, P.Y., Steinkraus, D., Platt, J.: Best practices for convolutional neural networks applied to visual document analysis. Institute of Electrical and Electronics Engineers, Inc. August 2003Google Scholar
  16. 16.
    Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual u-net. CoRR abs/1711.10684 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Eric Kerfoot
    • 1
    Email author
  • Esther Puyol Anton
    • 1
  • Bram Ruijsink
    • 1
  • James Clough
    • 1
  • Andrew P. King
    • 1
  • Julia A. Schnabel
    • 1
  1. 1.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK

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