Spatiotemporal Decomposition in Object-Space along Reconstruction in Emission Tomography

  • Xavier Hubert
  • Dominique Chambellan
  • Samuel Legoupil
  • Régine Trébossen
  • Jean-Robert Deverre
  • Nikos Paragios
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)


Emission tomography has provided a new insight in brain mechanisms past years. Although reconstructions are nowadays mostly static, trend is going toward dynamic acquisitions and reconstructions. This opens a new range of investigations, for instance for drugs discovery. Indeed new drugs are studied through the dynamic ability of tissues to catch them. However, it is required to know radiotracer concentration of blood that irrigates tissues in order to draw conclusions on potentials of these drugs. This concentration is called ’input function’ and this paper presents a new method for measuring it in a non-invasive way.

Our new method relies on simultaneous estimations of vessels kinetics and vessels spatial distribution. These estimations are performed during the reconstruction process and take into account the statistical nature of measured signals. Indeed, this method is based on the maximisation of the likelihood of counts in detectors. It takes advantages of a non-negative matrix factorisation which separate spatial and temporal components. Results are very promising, since it estimates arterial input function accurately although object emits just a limited amount of photons, especially within the first minutes.


Input Function Arterial Input Function Nonnegative Matrix Factorization Nonnegative Matrix Project Gradient Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Gallezot, J.D., et al.: Quantification of cerebral nicotinic acetylcholine receptors by PET using 2-[(18)F]fluoro-A-85380 and the multiinjection approach. Journal of Cerebral Blood Flow & Metabolism (2007)Google Scholar
  2. 2.
    Eriksson, L., et al.: Automated blood sampling systems for Positron Emission Tomography. IEEE Trans. Nucl. Sc. 35(1) (1988)Google Scholar
  3. 3.
    Naganawa, M., Kimura, Y., Ishii, K., Oda, K., Ishiwata, K.: Temporal and spatial blood information estimation using Bayesian ICA in dynamic cerebral Positron Emission Tomography. Digital Signal Processing 17(5) (2007)Google Scholar
  4. 4.
    Reader, A., et al.: Joint estimation of dynamic PET images and temporal basis functions using fully 4D ML-EM. Physics in Medicine and Biology 51 (2006)Google Scholar
  5. 5.
    Li, Q., et al.: A fast fully 4-D incremental gradient reconstruction algorithm for list mode PET data. IEEE Trans. Med. Imag. 26(1) (2007)Google Scholar
  6. 6.
    Lee, D.D., Seung, H.S.: Algorithms for Non-negative Matrix Factorization. In: NIPS, pp. 556–562 (2000)Google Scholar
  7. 7.
    Lee, J.S., et al.: Non-negative matrix factorization of dynamic images in nuclear medicine. In: Nucl. Sc. Symp. Conf. Record (2002)Google Scholar
  8. 8.
    Lin, C.J.: Projected gradient methods for Nonnegative Matrix Factorization. Neural Comput. 19(10), 2756–2779 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Cannon, T.M., Fenimore, E.E.: Coded aperture imaging with uniformly redundant arrays. Appl. Opt. 17(3), 337–347 (1978)CrossRefGoogle Scholar
  10. 10.
    Finger, M.H., Prince, T.A.: Hexagonal uniformly redundant arrays for coded-aperture imaging. In: Proc. 19th Int. Cosmic Ray Conf., pp. 295–298 (1985)Google Scholar
  11. 11.
    Buvat, I., Lazaro, D.: Monte-Carlo simulations in emission tomography with GATE: An overview. N.I.M. in Physics Research A 569(2) (2006)Google Scholar
  12. 12.
    Hubert, X., et al.: Conjoint use of coded-aperture collimators and MLEM algorithm: towards large blood vessels reconstruction at 511 keV. In: IEEE Intern. Symp. Biomed. Imag. (2008)Google Scholar
  13. 13.
    Birgin, E.G., Martínez, J.M., Raydan, M.: Nonmonotone spectral projected gradient methods on convex sets. SIAM J. on Optimization 10(4), 1196–1211 (2000)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xavier Hubert
    • 1
    • 3
  • Dominique Chambellan
    • 1
  • Samuel Legoupil
    • 1
  • Régine Trébossen
    • 2
  • Jean-Robert Deverre
    • 2
  • Nikos Paragios
    • 3
    • 4
  1. 1.LIST, Laboratoire Images et Dynamique, Gif/YvetteCEAFrance
  2. 2.CEA, I2BMOrsayFrance
  3. 3.Laboratoire MASEcole Centrale de ParisFrance
  4. 4.Equipe GALENINRIA-Saclay, Ile-de-FranceFrance

Personalised recommendations