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Bayesian reconstruction of functional images using registered anatomical images as priors

  • G Gindi
  • M Lee
  • A Rangarajan
  • I G Zubal
2. Incorporation Of Priors In Tomographic Reconstraction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 511)

Abstract

We propose a Bayesian method whereby MAP estimates of functional (PET and SPECT) images may be reconstructed with the aid of prior information derived from registered anatomical (CT and MRI) images of the same slice. Our prior information consists of significant anatomical boundaries that are likely to correspond to discontinuities in an otherwise spatially smooth radionuclide distribution. Our algorithm, like others proposed recently, seeks smooth solutions with occasional discontinuities; the contribution here is the inclusion of a coupling term that influences the creation of discontinuities in the vicinity of the significant anatomical boundaries. Simulations on anatomically derived mathematical phantoms are presented. The reconstructions are greatly improved when the prior information is used.

Keywords

Multimodality SPECT CT PET MRI Data fusion Markov Random field 

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References

  1. Bailey DL, Hutton BF and Walker PJ (1987). Improved SPECT using simultaneous emission and transmission tomography. The Journal of Nuclear Medicine 28(5):845–851.Google Scholar
  2. Blake A and Zisserman A (1987). Visual Reconstruction. MIT Press, Cambridge, MA.Google Scholar
  3. Chen C, Pelizzari CA, Chen GTY, Cooper MD and Levin DN (1989). Image analysis of PET data with the aid of CT and MR images. In CN de Graaf and MA Viergever, editors, Information Processing in Medical Imaging pp. 601, Plenum Press.Google Scholar
  4. Chen CT, Johnson VE, Wong WH, Hu X, and Metz CE (April 1990). Bayesian image reconstruction in positron emission tomography. IEEE Transactions on Nuclear Science 37(2):636–641.Google Scholar
  5. Chen CT, Johnson VE, Hu X, Wong WH, and Metz CE (June 1990). PET image reconstruction with the use of correlated structural images (abstract). In JNM, Proceedings of the 37th Annual Meeting pp. 748.Google Scholar
  6. Gamble EB, Geiger D, Poggio T, and Weinshall D (1989). Integration of vision modules and labeling of surface discontinuities. IEEE Transactions on Systems, Man and Cybernetics. 19(6):1576–1581Google Scholar
  7. Geiger D and Girosi F (1989). Parallel and deterministic algorithms for MRFs: surface reconstruction and integration. Technical Report A. I. Memo, No. 1114, Artificial Intelligence Lab, M.I.T.Google Scholar
  8. Geman S and Geman D (1984). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE PAMI 6:721–741.Google Scholar
  9. Geman S and McClure DE (1985). Bayesian image analysis: an application to single photon emisson tomography. Proceedings of the American Statistical Association.Google Scholar
  10. Geman S and McClure DE (1987). Statistical methods for tomographic image reconstruction. In Proceedings of the 46th Session of the ISI, Bulletin of ISI.Google Scholar
  11. Geman D and Reynolds G (1990). Constrained restoration and the recovery of discontinuities. Technical Report, University of Massachusetts at Amherst.Google Scholar
  12. Gerlot P and Bizais Y (1987). Image registration: a review and a strategy for medical applications. In CN de Graaf and MA Viergever, editors, Information Processing in Medical Imaging pp. 81–89, Plenum Press.Google Scholar
  13. Hebert T and Leahy R (1989). A generalized EM algorithm for 3-D Bayesian reconstruction for Poisson data using Gibbs priors. IEEE Transactions on Medical Imaging 8(2):194–202.Google Scholar
  14. Johnson VE, Wong WH, Hu X, and Chen C (1989). Bayesian reconstruction of PET images using Gibbs priors. In DA Ortendahl and Jorge Llacer, editors, Information Processing in Medical Imaging pp. 15–28, Wiley-Liss.Google Scholar
  15. Kessler ML, Pitluck S, and Chen GTY (1986). Frontiers of radiation oncology, proceedings of WCCF workshop on advances in treatment planning.Google Scholar
  16. Leahy R, Hebert T, and Lee R (1989). Applications of Markov random fields in medical imaging. In DA Ortendahl and Jorge Llacer, editors, Information Processing in Medical Imaging pp. 1–14, Wiley-Liss.Google Scholar
  17. LeClerc YG (1989). Constructing simple stable descriptions for image partitioning. International Journal of Computer Vision vol.3:73–102.Google Scholar
  18. Liang Z, Jaszczak R, Floyd C, and Greer K (1989). A spatial interaction model for statistical image processing. In DA Ortendahl and Jorge Llacer, editors, Information Processing in Medical Imaging pp. 29–44, Wiley-Liss.Google Scholar
  19. Metropolis N, Rosenbluth A, Rosenbluth M, Teller A and Teller E (1953). Equation of state calculations by fast computing machines. Journal of Physical Chemistry 21:1087–1091.Google Scholar
  20. Poggio T, Gamble EB, and Little JJ (1988). Parallel integration of vision modules. Science 242:436–439.Google Scholar
  21. Rangarajan A and Chellappa R (1990). Adiabatic approximation as a tool in image estimation. In Parallel Architectures for Image Processing SPIE 1246, Santa Clara, CA.Google Scholar
  22. Simchony T, Chellappa R, and Lichtenstein Z (1990). Relaxation algorithms for map estimation of grey-level images with multiplicative noise. IEEE Transactions on Information Theory 36(3):608–613.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • G Gindi
    • 1
    • 2
  • M Lee
    • 2
  • A Rangarajan
    • 1
    • 3
  • I G Zubal
    • 1
  1. 1.Division of Imaging Science Department of Diagnostic RadiologyYale UniversityNew Haven
  2. 2.Department of Electrical EngineeringYale UniversityYale Station, New Haven
  3. 3.Department of Computer ScienceYale UniversityNew Haven

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