Fast Reconstruction of Accelerated Dynamic MRI Using Manifold Kernel Regression

  • Kanwal K. Bhatia
  • Jose Caballero
  • Anthony N. Price
  • Ying Sun
  • Jo V. Hajnal
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)


We present a novel method for fast reconstruction of dynamic MRI from undersampled k-space data, thus enabling highly accelerated acquisition. The method is based on kernel regression along the manifold structure of the sequence derived directly from k-space data. Unlike compressed sensing techniques which require solving a complex optimisation problem, our reconstruction is fast, taking under 5 seconds for a 30 frame sequence on conventional hardware. We demonstrate our method on 10 retrospectively undersampled cardiac cine MR sequences, showing improved performance over state-of-the-art compressed sensing.


Compress Sensing Dynamic Magnetic Resonance Imaging Manifold Learning Magnetic Resonance Imaging Acquisition Complex Optimisation Problem 
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  1. 1.
    Axel, L., Sodickson, D.K.: The need for speed: Accelerating CMR imaging assessment of cardiac function. JACC Cardiovasc. Imaging 7(9), 893–895 (2014)CrossRefGoogle Scholar
  2. 2.
    Baraniuk, R.G., Wakin, M.B.: Random projections of smooth manifolds. Foundations of Computational Mathematics 9, 51–77 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bhatia, K.K., Rao, A., Price, A.N., Wolz, R., Hajnal, J., Rueckert, D.: Hierarchical manifold learning for regional image analysis. IEEE T. Med. Im. 33, 444–461 (2014)CrossRefGoogle Scholar
  5. 5.
    Caballero, J., Price, A.N., Rueckert, D., Hajnal, J.: Dictionary learning and time sparsity for dynamic mr data reconstruction. IEEE T. Med. Im. 33, 979–994 (2014)CrossRefGoogle Scholar
  6. 6.
    Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. International Journal of Computer Vision 90, 255–266 (2010)CrossRefGoogle Scholar
  7. 7.
    Lustig, M., Donoho, D., Pauly, J.M.: Sparse mri: The application of compressed sensing for rapid MR imaging. Medical Image Analysis 58(6), 1182–1195 (2007)Google Scholar
  8. 8.
    Madore, B., Hoge, W., Chao, T.: Retrospectively gated cardiac cine imaging with temporal and spatial acceleration. Mag. Resonance in Medicine 29, 457–469 (2011)Google Scholar
  9. 9.
    Otazo, R., Candes, E., Sodickson, D.K.: Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magnetic Resonance in Medicine 73(3), 1125–1136 (2015)CrossRefGoogle Scholar
  10. 10.
    Poddar, S., Lingala, S.G., Jacob, M.: Real-time cardiac MRI using manifold sensing. In: International Society of Magnetic Resonance in Medicine, p. 5309 (2014)Google Scholar
  11. 11.
    Tsao, J., Boesiger, P., Pruessmann, K.P.: k-t blast and k-t sense: Dynamic mri with high frame rate exploiting spatiotemporal correlations. MRM 50, 1031–1042 (2003)CrossRefGoogle Scholar
  12. 12.
    Usman, M., Vaillant, G., Schaefter, T., Prieto, C.: Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data. Magnetic Resonance in Medicine 72, 1130–1140 (2014)CrossRefGoogle Scholar
  13. 13.
    Wachinger, C., Yigitsoy, M., Navab, N.: Manifold learning for image-based breathing gating with application to 4D ultrasound. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 26–33. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Zhao, B., Haldar, J.P., Christodoulou, A.G., Liang, Z.P.: Image reconstruction from highly undersampled (k-t)-space data with joint partial separability and sparsity constraints. IEEE T. Med. Im. 31, 1809–1820 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kanwal K. Bhatia
    • 1
  • Jose Caballero
    • 1
  • Anthony N. Price
    • 2
  • Ying Sun
    • 3
  • Jo V. Hajnal
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonLondonUK
  2. 2.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  3. 3.Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore

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