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Analysis and Reconstruction of Medical Images Using Prior Information

  • Conference paper
Case Studies in Bayesian Statistics, Volume II

Part of the book series: Lecture Notes in Statistics ((LNS,volume 105))

Summary

We propose a Bayesian model for medical image analysis that permits prior structural information to be incorporated into the estimation of image features. Although the proposed methodology can be applied to a broad spectrum of images, we restrict attention here to emission computed tomography (ECT) images, and in particular single photon emission computed tomography (SPECT) images.

Inclusion of prior information regarding likely shapes of objects in the source distribution is accomplished using a hierarchical Bayesian model. A distinguishing feature of this model is that at the lowest level of the hierarchy, a distribution is specified over the class of all partitions of the discretized image scene. The markers used to identify these regions, called region identifiers, are assigned to each voxel (i.e. a volume element, similar to a pixel in 2D Images), and are the mechanism by which information regarding the locations of likely image structures is incorporated into the prior model. Importantly, such information can be incorporated in a non-deterministic fashion, thus permitting prior structural information to be modified by image data with minimal introduction of residual artifacts. Furthermore, the statistical model accommodates the formation of object structures not anticipated a priori, based soley on the observed likelihood function.

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References

  • Amit, Y., Grenander, U. and Piccioni, M. (1991), “Structural Image Restoration Through Deformable Templates”, Journal of the American Statistical Association, 86, 376–387.

    Article  Google Scholar 

  • Axelsson, B., Msaki, P. and Israelsson, A. (1984), “Subtraction of Compton-scattered photons in single-photon emission computerized tomography”, Journal of Nuclear Medicine 25, 490–494.

    Google Scholar 

  • Bailey D.L., Hutton, B.F. and Walker, P.J. (1987), “Improved SPECT using simultaneous emission and transmission tomography. Journal of Nuclear Medicine, 28, 844–851.

    Google Scholar 

  • Barrett, H.H. (1992), “Image Reconstruction and the Solution of Inverse Problems in Medical Imaging”, in Medical Images: Formation, Handling, and Evaluation, Springer-Verlag: Berlin, 3–42.

    Google Scholar 

  • Besag, J.E. (1972), “Nearest-neighbour Systems and the Auto-logistic Model of Binary Data”, Journal of the Royal Statistical Society, Ser. B, 34, 75–83.

    MathSciNet  MATH  Google Scholar 

  • Besag, J.E. (1974), “Spatial Interaction and the Statistical Analysis of Lattice Systems”, Journal of the Royal Statistical Society, Ser. B, 36, 192–225.

    MathSciNet  MATH  Google Scholar 

  • Besag, J.E. (1986), “On the Statistical Analysis of Dirty Pictures”, Journal of the Royal Statistical Society Ser. B, 48, 259–302.

    MathSciNet  MATH  Google Scholar 

  • Besag, J.E. (1989), “Towards Bayesian Image Analysis”, Journal of Applied Statistics, 16, 395–407.

    Article  Google Scholar 

  • Besag, J., Yorkr J. and Mollie, A. (1991), “Bayesian Image Restoration with Two Applications in Spatial Statistics”, Annals of the Institute of Statistical Mathematics, 43, 1–59.

    Article  MathSciNet  MATH  Google Scholar 

  • Bowsher, J.E. and Floyd, C.E. (1991), “Treatment of Compton Scattering in Maximum Likelihood, Expectation-Maximization Reconstructions of SPECT Images”, Journal of Nuclear Medicine, 32, 1285–1291.

    Google Scholar 

  • Bowsher J.E., Johnson, V.E. and Floyd, C.E. (1991), “Image Segmentation in Bayesian Reconstructions for Emission Computed Tomography”, Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference, 2006–2011.

    Google Scholar 

  • Budinger, T.F. and Gullberg, G.T. (1974), “Three dimensional Reconstruction in Nuclear Medicine Emission Imaging”, IEEE Transactions on Nuclear Science, 21, 2–20.

    Article  Google Scholar 

  • Chang, L.T. (1978), “A method for attenuation correction in radionuclide computed tomography”, IEEE Transactions on Nuclear Science, 25(2), 638–643.

    Article  Google Scholar 

  • Chen, C-T., Ouyang, X., Wong, W., Hu, X., Johnson, V., Ordonez, C. and Metz, C.E. (1991), “Sensor fusion in image reconstruction”, IEEE Transactions on Nuclear Science, 38, 687–692.

    Article  Google Scholar 

  • Dempster, A.P., Laird. N.M. and Rubin, D.B. (1977), “Maximum Likelihood from Incomplete Data via the EM Algorithm”, Journal of the Royal Statistical Society ser. B, vol. 39, pp. 1–38, 1977.

    MathSciNet  MATH  Google Scholar 

  • Derin, H. and Elliot, H. (1987), “Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields”, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-9, 1, 39–55.

    Article  Google Scholar 

  • Derin, H., Elliot, H., Cristi, R. and Geman, D. (1984), “Bayes Smoothing Algorithms for Segmentation of Binary Images Modeled by Markov Random Fields”, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6, 6, 707–720.

    Article  MATH  Google Scholar 

  • Drayer, B., Jaszczak, R., Friedman, A., et al. (1983) “In vivo quantitation of regional cerebral blood flow in glioma and cerebral infarction: validation of the HIPDm-SPECT method”, AJNR 4, 572–576.

    Google Scholar 

  • Evans, A.C., Beil, C, Marrett, S., Thompson, C.J. and Hakim, A. (1988), “Anatomical-functional correlation using an adjustable MRI-based region of interest atlas with positron emission tomography”, Journal Cerebral Blood Flow and Metabolism 8, 513–530.

    Article  Google Scholar 

  • Evans, A.C., Marret, S., Collins, L. and Peters, T.M. (1989), “Anatomical-correlative analysis of the human brain using three-dimensional imaging systems”, Proceedings of the Society for Industrial Engineering, 1092, 264–274.

    Google Scholar 

  • Evans, A.C., Marrett, S., Torrescorzo. J., Ku, S. and Collins, L. (1991), “MRI-PET correlation in three dimensions using a volume-of-interest (VOI) atlas”, Journal Cerebral Blood Flow and Metabolism, 11, A69–A78.

    Article  Google Scholar 

  • Feldkamp, L.A., Davis, L.C. and Kress, J.W. (1984), “Practical conebeam algorithms”, Journal Optical Society of America, 1, 612–619.

    Article  Google Scholar 

  • Floyd, C.E.J., Jaszczak, R.J. and Coleman, R.E. (1985), “Inverse Monte Carlo: A unified reconstruction algorithm for SPECT”, IEEE Transactions on Nuclear Science, 32, 779–85.

    Article  Google Scholar 

  • Frey, E.G. and Tsui, B.M.W. (1990), “Parameterization of the scatter response function in SPECT imaging using Monte Carlo simulation”, IEEE Transactions on Nuclear Science, 37, 1308–1315.

    Article  Google Scholar 

  • Frey, E.G., Tsui, B.M.W. and Perry, R. (1992), “Simultaneous acquisition of emission and transmission data for improved thallium-201 cardiac SPECT using a technetium-99m transmission source”, Journal Nuclear Medicine, 33(12), 2238–2245.

    Google Scholar 

  • Gelfand, A.E. and Smith, A.F.M. (1990), “Sampling-Based Approaches to Calculating Marginal Densities”, Journal of the American Statistical Association, 85, 398–409.

    Article  MathSciNet  MATH  Google Scholar 

  • Geman, S. and Geman, D. (1984), “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, pp. 721–741.

    Article  MATH  Google Scholar 

  • Geman, S. and McClure, D.E. (1985), “Bayesian Image Analysis: An Application to Single Photon Emission Tomography”, Proceedings of the American Statistical Association, Statistical Computing Section, 12–18.

    Google Scholar 

  • Geman, S. and McClure, D.E. (1987), “Statistical Methods for Tomographic Image Reconstruction”, Proceedings of the 46th Session of the ISI, Bulletin of the ISI 52.

    Google Scholar 

  • Genna, S. and Smith, A. (1988), “The development of ASPECT, an annular single crystal brain camera for high efficiency SPECT”, IEEE Transactions on Nuclear Science, 35, 654–658.

    Article  Google Scholar 

  • Gidas, B. and Hudson, M. (1991), “A Nonlinear Multi-Grid EM Algorithm for Emission Tomography”, preprint, Division of Applied Mathematics, Brown University.

    Google Scholar 

  • Gindi, G., Lee, M., Rangarajan, A. and Zubal, I.G. (1991), “Bayesian Reconstruction of Functional Images Using Registered Anatomical Images as Priors”, XII th International Conference on Information Processing and Medical Imaging, 121–131. Springer-Verlag: New York.

    Google Scholar 

  • Green, P.J. (1990), “Bayesian Reconstructions from Emission Tomography Data Using a Modified EM Algorithm”, IEEE Transactions on Medical Imaging, MI-9, 84–93.

    Google Scholar 

  • Greer, K.L., Harris, C.C. and Jaszczak, R.J., et al. (1987), “Transmission computed tomography data acquisition”, Journal of Medical Technology, 15(2), 53–56.

    Google Scholar 

  • Grenander, U. and Keenan, D.M. (1986), “On the Shape of Plane Images,” Theoretical Study of Biological Shape”, Division of Applied Mathematics, Brown University.

    Google Scholar 

  • Hawman, E.G. and Hsieh, J. (1986), “An astigmatic collimator for high sensitivity SPECT of the brain”, Journal of Nuclear Medicine, 27, 930.

    Google Scholar 

  • Hebert, T. and Leahy, R. (1989), “A Generalized EM Algorithm for 3D Bayesian Reconstruction from Poisson Data Using Gibbs Priors”, IEEE Transactions on Medical Imaging, 8, 194–202.

    Article  Google Scholar 

  • Hill, T.C., Holman, B.L., Lovett, R. et al. (1982), “Initial experience with SPECT (single-photon computerized tomography) of the brain using N-isopropyl I-123 p-iodoam-phetamine: Concise communication. Journal of Nuclear Medicine, 23, 191–195.

    Google Scholar 

  • Hoffman, E.H., Cutler, P.D., Digby, W.M. and Mazziotta, J.C. (1990), “3-D Phantom to Simulate Cerebral Blood Flow and Metabolic Images for PET”, IEEE Transactions on Nuclear Science, 37, 616–620.

    Article  Google Scholar 

  • Holman, B.L., Zimmerman, R.E. and Johnson, K.A., (1990), “Computer-assisted superimposition of magnetic resonance and Jbigh-resolutlon technetium-99m-HMPAO and thallium-201 SPECT images of the brain”, Journal Nuclear Medicine, 32, 1478–1484.

    Google Scholar 

  • Jaszczak, R., Huard, D., Murphy, P. and Burdine, J. (1976), “Radionuclide emission computed tomography with a scintillation camera. Journal of Nuclear Medicine, 17, 551.

    Google Scholar 

  • Jaszczak, R.J., Murphy, P.H., Huard, D. and Burdine, J.A. (1977), “Radionuclide emission computed tomography of the head with Tc-99m and a scintillation camera”, Journal of Nuclear Medicine, 18, 383–380.

    Google Scholar 

  • Jaszczak, R.J., Chang, L.T., Stein, N.A. and Moore, F.E. (1979), “Whole body single-photon emission computed tomography using dual, large field-of-view scintillation cameras”, Physics, Medicine, and Biology 24(6), 1123–1143.

    Article  Google Scholar 

  • Jaszczak, R.J., Chang, L.T. and Murphy, P.H. (1979a), “Single photon emission computed tomography using multi-slice fan beam collimators”, IEEE Transactions on Nuclear Science, 26, 610–618.

    Article  Google Scholar 

  • Jaszczak, R.J. and Coleman, R.E. (1980), “Selected processing techniques for scintillation camera based SPECT systems”, SPECT, 45–59.

    Google Scholar 

  • Jaszczak, R.J., Coleman, R.E. and Whitehead, F.R. (1981), “Physical factors affecting quantitative measurements using camera-based single photon emission computed tomography”, IEEE Transactions on Nuclear Science, 28, 69–80.

    Article  Google Scholar 

  • Jaszczak, R.J., Greer, K.L., Floyd, C.E.J., Harris, C.C. and Coleman, R.E. (1984), “Improved SPECT quantification using compensation for scattered photons”, Journal of Nuclear Medicine, 25, 893–900.

    Google Scholar 

  • Jaszczak, R.J., Floyd, C.E. and Coleman, R.E. (1985), “Scatter compensation techniques for SPECT”, IEEE Transactions on Nuclear Science, NS-32, 786–793.

    Google Scholar 

  • Jaszczak, R.J., Floyd, C.E., Manglos, S.H., Greer, K.L. and Coleman, R.E. (1986), “Cone beam collimation for SPECT: Analysis, simulation and image reconstruction using filtered backprojection”, Medical Physics, 13(4), 484–489.

    Article  Google Scholar 

  • Jaszczak, R.J., Greer, K.L. and Coleman, R.E. SPECT using a specially designed cone beam collimator. Journal of Nuclear Medicine 1988a;29, 1398–1405.

    Google Scholar 

  • Jaszczak, R.J., Greer, K.L., Manglos, S.H., Floyd, C.E. and Coleman, R.E. (1988b), “Imaging characteristics of a high resolution cone beam collimator”, IEEE Transactions on Nuclear Science, NS-35, 644–648.

    Google Scholar 

  • Jaszczak, R.J., Gilland, D.R., Hanson, M.W., Jang, S., Greer, K.L., and Coleman, R.E. (1993), “Fast transmission CT for determining attenuation maps using a collimated, line source, rotatable air-copper-lead attenuators, and fan beam collimation. Journal of Nuclear Medicine.

    Google Scholar 

  • Johnson, V.E., Wong, W.H., Hu, X., and Chen, C.T. (1990), “Bayesian Restoration of PET images using Gibbs Priors”, XI th International Conference On Information Processing in Medical Imaging, Wiley- Liss, 15–28.

    Google Scholar 

  • Johnson, V.E., Wong, W.H., Hu, X., and Chen, C.T. (1991), “Aspects of Image Restoration Using Gibbs Priors: Boundary Modeling, Treatment of Blurring, and Selection of Hyperparameters”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 5, 412–425.

    Article  Google Scholar 

  • Johnson, V.E., (1992a), “A Model for Segmentation and Analysis of Noisy Images”, ISDS DP 92–A26, to appear in June 1994 Journal of the American Statistical Association.

    Google Scholar 

  • Johnson, V.E., Wong, W.H., Hu, X., and Chen, C.T. (1992b), “Data Augmentation Schemes Applied to Image Restoration”, in Medical Imaging: Formation, Handling, and Evaluation, Springer-Verlag: Berlin, 345–360.

    Google Scholar 

  • Johnson, V.E., (1993), “A Framework for Incorporating Prior Information into the Reconstruction of Medical Images”, in XIIth International Conference on Information Processing and Medical Imaging, Springer-Verlag: New York.

    Google Scholar 

  • Kapouleas, I., Alavi, A., Alves, W.M., Gur, R.E. and Weiss, D.W. (1991), “Registration of three-dimensional MR and PET of the human brain without markers”, Radiology, 181, 731–739.

    Google Scholar 

  • Keyes, W.I. (1976), “A practical approach to transverse-section gamma-ray imaging”, British Journal of Radiology, 49, 62–70.

    Article  Google Scholar 

  • Keyes, J.W., Orlandea, N., Heetderks, W.J., Leonard, P.F. and Rogers, W.L. (1977), “The humogotron: A scintillation-camera transaxial tomography. Journal of Nuclear Medicine, 18(4), 381”387.

    Google Scholar 

  • Koral, K.F., Wang, X.Q., Rogers, W.L., Clinthorne, N.H. and Wang, X.H. (1988), “SPECT Compton- scattering correction by analysis of energy spectra”, Journal of Nuclear Medicine, 29(2), 195–202.

    Google Scholar 

  • Kung, H.F., Molnar, M., Billings, J., Wicks, R. and Blau, M. (1984), “Synthesis and biodistribution of neutral lipid-soluble Tc-99m complexes that cross the blood-brain barrier. Journal of Nuclear Medicine, 25, 326–332.

    Google Scholar 

  • Kung, H.F., Tramposch, K.M. and Blau, M. (1983), “A new brain perfusion imaging agent:[I-123]HIPDM:N, N, N -Trimethyl-N -[2-Hydroxy-3-Mthyl-5-Iodobenzyl]l,3, Propanediamine. Journal Nuclear Medicine, 24, 66–72.

    Google Scholar 

  • Lange, K. and Carson, R. (1984), “EM Reconstruction Algorithms for Emission and Transmission Tomography”, Journal of Computer Assisted Tomography, 8, 306–318.

    Google Scholar 

  • Leahy, R. and Yan, X. (1991), “Incorporation of Anatomical MR Data for Improved Functional Imaging with PET”, XII th International Conference on Information Processing and Medical Imaging, 105–120. Springer-Verlag: New York.

    Google Scholar 

  • Levin, D.N., Hu, X. and Tan, K.K. (1989), “The brain: integrated three-dimensional display of MR and PET images”, Radiology, 172, 783–789.

    Google Scholar 

  • Lim, C.B., Chang, L.T. and Jaszczak, R.J. (1980), “Performance analysis of three camera configurations for single photon emission computed tomography”, IEEE Transactions on Nuclear Science, NS-27(1), 559–68.

    Article  Google Scholar 

  • Lim, C.B., Gottschalk, S., Walker, R. and Schreiner, R. (1985), “Triangular SPECT system for 3-D total organ volume imaging”, IEEE Transactions on Nuclear Science, NS-32, 741–747.

    Google Scholar 

  • Llacer, J., Veklerov, E. and Nunez, J. (1991), “Preliminary examination of the use of case specific medical information as “prior” in Bayesian reconstruction”, In: Colchester ACF, Hawkes DJ, eds. Information Processing in Medical Imaging, 12th International Conference Berlin: Springer-Verlag, 81–93.

    Chapter  Google Scholar 

  • Manglos, S.H., Jaszczak, R.J., Floyd, C.E, Hahn, L.J., Greer, K.L. and Coleman, R.E. (1987), “Nonisotropic attenuation in SPECT: Phantom tests of quantitative effects and compensation techniques”, Journal Nuclear Medicine, 28(10), 1584–91.

    Google Scholar 

  • Manglos, S.H., Bassano, D.A. and Thomas, F.D. (1991), “Cone beam transmission computed tomography for nonuniform attenuation compensation of SPECT images. Journal of Nuclear Medicine, 32, 1813–1820.

    Google Scholar 

  • Marr, D., (1976), “Early Processing of Visual Information”, Philosophical Transactions of the Royal Society of London, Ser. B, 275, 483–524.

    Article  Google Scholar 

  • Mazziotta, J.C. and Koslow, S.H. (1987), “Assessment of goals and obstacles in data acquisition and analysis from emission tomography: report of a series of international workshops. Journal Cerebral Blood Flow and Metabolism, 7, S1–S31.

    Article  Google Scholar 

  • Meltzer, C.C., Bryan, R.N. and Holcomb, H.H. (1990), “Anatomical localization for PET using MR imaging”, Journal Computer Assisted Tomography, 14, 418–426.

    Article  Google Scholar 

  • Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. (1953), “Equations of State Calculations by Fast Computing Machines”, Journal of Chemistry and Physics, 21, 1087–1092.

    Article  Google Scholar 

  • Miuri, S., Kanno, I., Iida, EL, et al. (1988), “Anatomical adjustments in brain positron emission tomography using CT images”, Journal Computer Assisted Tomography, 12, 363–367.

    Article  Google Scholar 

  • Mueller-Gaerter, H.W., Links, J.M., Prince, J.L., Bryan, R.N., McVeigh, E., Leal, J.P., Davatzikos, C. and Frost, J.J. (1992), “Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects. Journal Cerebral Blood Flow and Metabolism, 12, 571–583.

    Article  Google Scholar 

  • Muehllehner, G. and Wetzel, R.A. (1971), “Section imaging by computer calculation. Journal of Nuclear Medicine, 12(2), 76–84.

    Google Scholar 

  • Nohara, N., Murayama, H. and Tanaka, E. (1987), “Single photon emission tomography with increased sampling density at central region of field-of-view”, IEEE Transactions on Nuclear Science, 34, 359–363.

    Article  Google Scholar 

  • Obrist, W.D., Thompson, H.K., Wang, H.S. and Wilkinson, W.E. (1975), “Regional cerebral blood flow estimated by 133xenon inhalation”, Stroke, 6, 245.

    Article  Google Scholar 

  • Pelizzari, C.A., Chen, G.T.Y., Spelbring, D.R., Weichselbaum, R.R. and Chen, C.T. (1989), “Accurate three-dimensional registration of CT, PET, and/or MR images of the brain”, Journal Computer Assisted Tomography, 13, 20–26.

    Article  Google Scholar 

  • Pelizzari, C.A., Evans, A.C., Neelin, P., Chen, C.T., and Marrett, S. (1991), “Comparison of two methods for 3D registration of PET and MR1 images”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 13, 221–223.

    Google Scholar 

  • Qian, J., Johnson, V.E., Bowsher, J.E. and Jaszczak, R.J. (1992), “A Deterministic Approach to the Posterior Distribution in a Bayesian Imaging Model”, ISDS Discussion Paper, Duke University.

    Google Scholar 

  • Ripley, B. (1987), Stochastic Simulation, New York: John Wiley & Sons.

    Book  MATH  Google Scholar 

  • Shepp, L. and Vardi, Y. (1982), “Maximum Likelihood Reconstruction for Emission Tomography”, IEEE Transactions on Medical Imaging, MI-1, 113–122.

    Google Scholar 

  • Smith, M.F., Floyd, C.E., Jaszczak, R.J., and Coleman, R.E. (1992), “Evaluation of Projection Pixel-Dependent and Pixel-Independent Scatter Correction in SPECT”, IEEE Transactions on Nuclear Science, 39, 1099–1105.

    Article  Google Scholar 

  • Smith, M.F. and Jaszczak, R.J. (1994), “Generalized Dual Energy Window Scatter Compensation in Spatially-Varying Media for SPECT”, Physics in Medicine and Biology, 39, 531–1994.

    Article  Google Scholar 

  • Snyder, D. and Miller, M. (1985), “The Use of Sieves to Stabilize Images Produced with the EM Algorithm for Emission Tomography”, IEEE Transactions on Nuclear Science, NS-32, 3864–3870.

    Google Scholar 

  • Tanner, M. and Wong, W.H. (1987), “Calculation of Posterior Distributions by Data Augmentation”, Journal of the American Statistical Association, 82, 528–540.

    Article  MathSciNet  MATH  Google Scholar 

  • Tsui, B.M.W., Gullberg, G.T., Edgerton, E.R., Gilland, D.R., Perry, J.R. and McCartney, W.H. (1986), “Design and clinical utility of a fan beam collimator for SPECT imaging of the head. Journal Nuclear Medicine, 27,810–819.

    Google Scholar 

  • Tsui, B.M.W., Gullberg, G.T. and Edgerton, E.R. (1989), “Correction of nonuniform attenuation in cardiac SPECT imaging”, Journal of Nuclear Medicine 30, 497–507.

    Google Scholar 

  • Tung, C-H., Gullberg, G.T., Zeng, G.L., Christian, P.E., Datz, FX. and Morgan, H.T. (1992), “Non-uniform attenuation correction using simultaneous transmission and emission converging tomography”, IEEE Transactions on Nuclear Science, 39(4), 1134–1143.

    Article  Google Scholar 

  • Turkington, T.G., Jaszczak, R.J., Greer, K.L., Coleman, R.E. and Pelizzari, C.A. (1992), “Correlation of SPECT images of a three-dimensional brain phantom using a surface fitting technique”, IEEE Transactions on Nuclear Science, 39, 1460–1463.

    Article  Google Scholar 

  • Turkington, T.G., Jaszczak, R.J., Pelizzari, C.A., Harris, C.C., MacFall, J.R., Hoffman, J.M. and Coleman, R.E. (1993), “Accuracy of registration of PET, SPECT, and MR images of a brain phantom”, Journal of Nuclear Medicine, in press.

    Google Scholar 

  • Valentino, D.J., Mazziotta, J.C. and Huang, H.K. (1991), “Volume rendering of multimodal images: Application to MRI and PET imaging of the human brain”, IEEE Transactions on Medical Imaging, 10, 554–562.

    Article  Google Scholar 

  • Vogl, G., Schwer, C, Jauch, M., Wietholter, H., Kindermann, U., Muller-Schauenburg (1989), “A simple superposition method for anatomical adjustments of CT and SPECT images”, Journal Computer Assisted Tomography, 13, 929–931.

    Article  Google Scholar 

  • Vardi, Y., Shepp, L. and Kaufman, L. (1985) “A Statistical Model for Positron Emission Tomography”, Journal of the American Statistical Association, 80, 8–25.

    Article  MathSciNet  MATH  Google Scholar 

  • Yanch, J.C, Flower, M.A. and Webb, S. (1990), “Improved quantification of radionuclide uptake using deconvolution and windowed subtraction techniques for scatter compensation in single photon emission computed tomography”, Medical Physics, 17(6), 1011–22.

    Article  Google Scholar 

  • Almeida, M. and B. Gidas (1993). “A Variational Method for Estimating the Parameters of MRF from Complete or Incomplete Data”, Ann. of Appl. Prob. 3, 103–136.

    Article  MathSciNet  MATH  Google Scholar 

  • Geman, S. and C. Graffigne (1987). “Markov Random Field Image Models and their Applications to Computer Vision”, in Proceeding of the International Congress of Mathematics, AMS, ed. A.M. Gleason.

    Google Scholar 

  • Geman, S. and D. McClure (1985). “Bayesian Image Analysis: An Application to SPECT”, Proceedings of the American Statistical Association, Statistical Computing Session, 12–18.

    Google Scholar 

  • Gidas, B. (1994). “Metropolis-type Monte Carlo Simulators Algorithms and Simulated Annealing”, Current Topics in Probability Theory, CRC Press, April 1995. Ed.: L. Snell.

    Google Scholar 

  • Green, P. J. (1990). “Bayesian Reconstruction from Emission Tomography Data Using a Modified EM Algorithm”, EEEE Trans. Med. Imag. MI-9, 84–93.

    Google Scholar 

  • Sokal, A. D. (1989). “Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms”, Lecture Notes, Lausanne.

    Google Scholar 

  • Swendsen, R. H. and J-S. Wang (1987). “Nonuniversal Critical Dynamics in Monte Carlo Simulations”, Phys. Rev. Letters, 58, 86–88.

    Article  Google Scholar 

  • Avkroyd, R.G. and Green, P.J. (1991) Global and local priors, and the location of lesions using gamma camera imagery. Phil Trans. Royal Society London (A) 10: 381–407.

    Google Scholar 

  • English, R.J., Brown, S.E. (1986) S PECT, Single Photon Emission Computed Tomography: A Primer. New York: The Society of Nuclear Medicine.

    Google Scholar 

  • Herman, G.T. (1980) Image Reconstruction From Projections. New York: Academic Press.

    MATH  Google Scholar 

  • Russ, J.C. (1995) The Image Processing Handbook, Second Edition. Boca Raton, Florida: CRC Press.

    Google Scholar 

  • Serra, J. (1982) Image Analysis and Mathematical Morphology. New York: Academic Press.

    MATH  Google Scholar 

  • Soille. P. and Rivest, J-F. (1993) Principles and Applications of Morphological Image Analysis. Sydney, Australia: Image Analysis Project, CSIRO.

    Google Scholar 

  • Sternberg, S.R. (1986) Grayscale morphology. Com. Vis. Graph. Im. Proc., 35: 333–335.

    Article  Google Scholar 

  • Herman, G. (Editor) (1979) Image Reconstruction from Projections, Implementation and Applications, Springer-Verlag: Heidelberg, 1979.

    Google Scholar 

  • Jaszczak, R.J., Li, J., Wang, H., Zalutsky, M.R., and Coleman, R.E. (1994) “Pinhole collimation for ultra-high-resolution, small-field-of-view SPECT”, Physics, Medicine, and Biology, 39, 425–437.

    Article  Google Scholar 

  • Potts, R.B., (1952), “Some generalised order-disorder transformations”, Proceedings of the Cambridge Philosophical Society, 48, 106–109.

    Article  MathSciNet  MATH  Google Scholar 

  • Turkington T.G., Jaszczak, R.J., Pelizarri, C.A., Harris, C.C., MacFall, J.R., Hoffman, J.M., and Coleman, R.E. (1993), “Accuracy of registration of PET, SPECT, and MR images of a brain phantom”, Journal of Nuclear Medicine, 34:1587–1594, 1993.

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Authors and Affiliations

  1. Duke University, USA

    Valen Johnson & Ronald Jaszczak

  2. Duke University Medical Center, USA

    James Bowsher & Timothy Turkington

Authors
  1. Valen Johnson
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  2. James Bowsher
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  3. Ronald Jaszczak
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  4. Timothy Turkington
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Editor information

Editors and Affiliations

  1. Center for Statistical Sciences, Brown University, Box G-A416, 02912, Providence, RI, USA

    Constantine Gatsonis

  2. Division of Biostatistics, University of Minnesota - Twin Cities School of Public Health, 2221 University Avenue SE, 55414-3075, Minneapolis, MN, USA

    James S. Hodges

  3. Department of Statistics, Carnegie Mellon University, 15213, Pittsburgh, PA, USA

    Robert E. Kass

  4. Department of Operations Research, The George Washington University, 20052, Washington, DC, USA

    Nozer D. Singpurwalla

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© 1995 Springer-Verlag New York, Inc.

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Johnson, V., Bowsher, J., Jaszczak, R., Turkington, T. (1995). Analysis and Reconstruction of Medical Images Using Prior Information. In: Gatsonis, C., Hodges, J.S., Kass, R.E., Singpurwalla, N.D. (eds) Case Studies in Bayesian Statistics, Volume II. Lecture Notes in Statistics, vol 105. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2546-1_4

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  • DOI: https://doi.org/10.1007/978-1-4612-2546-1_4

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94566-8

  • Online ISBN: 978-1-4612-2546-1

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