Advertisement

Adaptive Sampling and Non Linear Reconstruction for Cardiac Magnetic Resonance Imaging

  • Giuseppe Placidi
  • Danilo Avola
  • Luigi Cinque
  • Guido Macchiarelli
  • Andrea Petracca
  • Matteo Spezialetti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8641)

Abstract

We show how an adaptive acquisition sequence and a non linear reconstruction can be efficiently used to reconstruct undersampled cardiac MRI data. We demonstrate that, by using the adaptive method and L 0-homotopic minimization, we can reconstruct an image with a number of samples which is very close to the sparsity coefficient of the image without knowing a-priori the sparsity of the image. We highlight two important aspects: 1) how the shape and the cardinality of the starting dataset influence the acquisition/reconstruction process; 2) how well the termination criteria allows to fit the optimal number of coefficients. The method is tested on MRI cardiac images and it is also compared to the weighted Compressed Sensing. All the experiments and results are reported and discussed.

Keywords

adaptive sampling compressed sensing non linear reconstruction MRI cardiac imaging image reconstruction imaging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    O’sullivan, J.: A fast sinc function gridding algorithm for fourier inversion in computer tomography. IEEE Transactions on Medical Imaging 4(4), 200–207 (1985)CrossRefGoogle Scholar
  2. 2.
    Placidi, G., Alecci, M., Sotgiu, A.: ω-space adaptive acquisition technique for magnetic resonance imaging from projections. Journal of Magnetic Resonance 143(1), 197–207 (2000)CrossRefGoogle Scholar
  3. 3.
    Placidi, G., Alecci, M., Sotgiu, A.: Theory of adaptive acquisition method for image reconstruction from projections and application to epr imaging. Journal of Magnetic Resonance, Series A 108(1), 50–57 (1995)CrossRefGoogle Scholar
  4. 4.
    Placidi, G.: MRI: essentials for innovative technologies. CRC Press (2012)Google Scholar
  5. 5.
    Placidi, G., Alecci, M., Colacicchi, S., Sotgiu, A.: Fourier reconstruction as a valid alternative to filtered back projection in iterative applications: implementation of fourier spectral spatial epr imaging. Journal of Magnetic Resonance 134(2), 280–286 (1998)CrossRefGoogle Scholar
  6. 6.
    Candès, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52(2), 489–509 (2006)CrossRefzbMATHGoogle Scholar
  7. 7.
    Donoho, D.L.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Lustig, M., Donoho, D., Pauly, J.M.: Sparse mri: The application of compressed sensing for rapid mr imaging. Magnetic Resonance in Medicine 58(6), 1182–1195 (2007)CrossRefGoogle Scholar
  9. 9.
    Elad, M.: Sparse and redundant representations: from theory to applications in signal and image processing. Springer (2010)Google Scholar
  10. 10.
    Usman, M., Prieto, C., Schaeffter, T., Batchelor, P.: k-t group sparse: A method for accelerating dynamic mri. Magnetic Resonance in Medicine 66(4), 1163–1176 (2011)CrossRefGoogle Scholar
  11. 11.
    Trzasko, J.D., Manduca, A.: A fixed point method for homotopic l0-minimization with application to mr image recovery. In: Medical Imaging, International Society for Optics and Photonics, pp. 69130F–69130F (2008)Google Scholar
  12. 12.
    Trzasko, J., Manduca, A.: Highly undersampled magnetic resonance image reconstruction via homotopic-minimization. IEEE Transactions on Medical imaging 28(1), 106–121 (2009)CrossRefGoogle Scholar
  13. 13.
    Lustig, M.: Sparse MRI. ProQuest (2008)Google Scholar
  14. 14.
    Wang, Z., Arce, G.R.: Variable density compressed image sampling. IEEE Transactions on Image Processing 19(1), 264–270 (2010)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Ciancarella, L., Avola, D., Placidi, G.: Adaptive sampling and reconstruction for sparse magnetic resonance imaging. In: Computational Modeling of Objects Presented in Images, pp. 115–130. Springer (2014)Google Scholar
  16. 16.
    Ciancarella, L., Avola, D., Marcucci, E., Placidi, G.: A hybrid sampling strategy for sparse magnetic resonance imaging. Computational Modelling of Objects Represented in Images III: Fundamentals, Methods and Applications, 285 (2012)Google Scholar
  17. 17.
    Pipe, J.G., et al.: Motion correction with propeller mri: application to head motion and free-breathing cardiac imaging. Magnetic Resonance in Medicine 42(5), 963–969 (1999)CrossRefGoogle Scholar
  18. 18.
    Arfanakis, K., Tamhane, A.A., Pipe, J.G., Anastasio, M.A.: k-space undersampling in propeller imaging. Magnetic Resonance in Medicine 53(3), 675–683 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Giuseppe Placidi
    • 1
  • Danilo Avola
    • 1
  • Luigi Cinque
    • 2
  • Guido Macchiarelli
    • 1
  • Andrea Petracca
    • 1
  • Matteo Spezialetti
    • 1
  1. 1.Department of Life, Health and Environmental SciencesUniversity of L’AquilaL’AquilaItaly
  2. 2.Department of Computer ScienceSapienza UniversityRomeItaly

Personalised recommendations