Abstract
This work explores a fully-automated algorithm for estimation of the uptake of radio-pharmaceutical in brain MR-PET imaging. The algorithm is based on a model of the pharmaceutical uptake coupled with probabilistic models of the PET and MR acquisition systems. In contrast to algorithms that attempt to correct for the Partial Volume Effect (PVE), the problem is tackled here in the reconstruction by means of a probabilistic model of the pharmaceutical uptake. We make use of Hybrid Bayesian Networks to describe the joint probabilistic model and to obtain an efficient optimisation algorithm. We describe solutions adopted in order to mitigate the effect of local maxima and to reduce the sensitivity to the initialisation of the parameters, rendering the algorithm fully automatic. The algorithm is evaluated on simulated MR-PET data and on the reconstruction of clinical PET FDG acquisitions.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Green, P.G.: Bayesian Reconstructions From Emission Tomography Data Using a Modified EM Algorithm. IEEE Trans. on Med. Imag. 9(1), 84–93 (1990)
Nuyts, J., Baete, K., Bequ, D., Dupont, P.: Comparison between MAP and postprocessed ML for image reconstruction in emission tomography when anatomical knowledge is available. IEEE Trans. on Med. Imag. 24(5), 667–675 (2005)
Moghbel, M.C., Saboury, B., Basu, S., Metzler, S.D., Torigian, D.A., Langstrom, B., Alavi, A.: Amyloid-β imaging with PET in Alzheimers disease: is it feasible with current radiotracers and technologies? E. J. of Nuc. Med. and Mol. Im. 39(2) (2012)
Pedemonte, S., Bousse, A., Hutton, B.F., Arridge, S., Ourselin, S.: 4-D Generative Model for PET/MRI Reconstruction. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 581–588. Springer, Heidelberg (2011)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated Model-Based Bias Field Correction of MR Images of the Brain. IEEE Trans. on Med. Imag. 18(10), 885–896 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pedemonte, S., Cardoso, M.J., Arridge, S., Hutton, B.F., Ourselin, S. (2012). Steady-State Model of the Radio-Pharmaceutical Uptake for MR-PET. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-33415-3_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33414-6
Online ISBN: 978-3-642-33415-3
eBook Packages: Computer ScienceComputer Science (R0)