Model-based image reconstruction from time-resolved diffusion data

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 3034, Issue , 1997, Pages 369-380

  Saquib, Suhail S ^a   Hanson, Kenneth M ^b   Cunningham, Gregory S ^b  

^a PURDUE UNIVERSITY   (United States)

^b LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

Adjoint differentiation; Bayesian estimation; Diffusion; Finite difference; MAP; Markov random field; Optical tomography

Indexed keywords

BAYESIAN NETWORKS; DIAGNOSTIC RADIOGRAPHY; DIFFUSION; DIGITAL IMAGE STORAGE; HIDDEN MARKOV MODELS; IMAGE PROCESSING; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; IMAGING SYSTEMS; INVERSE PROBLEMS; OPTICAL DATA PROCESSING; OPTICAL TOMOGRAPHY; PARAMETER ESTIMATION; RANDOM PROCESSES; REPAIR; RESTORATION; TOMOGRAPHY;

A POSTERIORI; ADJOINT DIFFERENTIATION; AND SCATTERINGS; BAYESIAN ESTIMATION; BAYESIAN FRAMEWORKS; COMPUTATIONALLY EFFICIENT; DIFFUSED LIGHTS; DIFFUSION DATUMS; DIFFUSION MODELS; EDGE-PRESERVING; FINITE-DIFFERENCE; GAUSSIAN MARKOV RANDOM FIELDS; IMPLICIT METHODS; MARKOV RANDOM FIELD; MODEL-BASED; PAPER ADDRESSES; PERTURBATION APPROACHES; SIMULATED DATUMS; TIME STEPS; TIME USING; TIME-RESOLVED; TRIDIAGONAL MATRIXES;

MEDICAL IMAGING;

EID: 57649237824     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.274123     Document Type: Conference Paper

References (20)

1. G. Muller, B. Chance, R. Alfano, S. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg, and P. van der Zee, eds., Medical Optical Tomography: Functional Imaging and Monitoring, vol. IS 11, SPIE press, Bellingham, WA, 1993.
2. S. Flock, M. Patterson, B. Wilson, and D. Wyman, "Monte Carlo modeling of light propagation in highly scattering tissues - I: Model predictions and comparison with diffusion theory," IEEE Trans. Biomed. Eng. 36(12), pp. 1162-1168, 1989.
3. Y. Wang, J. Chang, R. Aronson, R. Barbour, H. Graber, and J. Lubowsky, "Imaging of scattering media by diffusion tomography: an iterative perturbative approach," in Physiological Monitoring and Early Detection Diagnostic Methods, Proc. SPIE 1641, pp. 58-71, 1992.
4. It. Barbour, H. Graber, Y. Wang, J. Chang, and R. Aronson, "A perturbation approach for optical diffusion tomography using continuous-wave and time-resolved data," in Medical Optical Tomography: Functional Imaging and Monitoring, vol. IS 11, pp. 87-120, SPIE Press, 1993.
5. J. Chang, H. Graber, and R. Barbour, "Image reconstruction of targets in random media from continuous wave laser measurements and simulated data," in Advances in Optical Imaging and Photon Migration, R. R. Alfano, ed., OSA Proc. 21, pp. 193-201, 1994.
6. B. B. Das, J. Dome, R. L. Barbour, H. L. Graber, J. Chang, M. Zevallos, F. Liu, and R. R. Alfano, "Analysis of time-resolved data for tomographical image reconstruction of opaque phantoms and finite absorbers in diffusive media," Proc. SPIE 2389, pp. 16-28, 1995.
7. S. R. Arridge, "Forward and inverse problems in time-resolved infrared imaging," in Medical Optical Tomography: Functional Imaging and Monitoring, vol. IS 11, pp. 35-64, SPIE Press, 1993.
8. R. Barbour, H. Graber, R. Aronson, and J. Lubowsky, "Imaging of subsurface regions of random media by remote sensing," Proc. SPIE 1431, pp. 192-203, 1991.
9. C. A. Bouman and K. Sauer, "A generalized Gaussian image model for edge-preserving MAP estimation," IEEE Trans. on Image Processing 2(3), pp. 296-310, 1993.
10. R. Model, R. Hunlich, M. Orit, and M. Waizel, "Image reconstruction for random media by diffusion tomography," Proc. SPIE 2389, pp. 400-410, 1995.
11. D. S. Burnett, Finite Element Analysis: From Concepts to Applications, Addison-Wesley, New York, 1987.
12. W. F. Ames, Numerical Methods for Partial Differential Equations, Academic Press, New York, 1977.
13. W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press, Cambridge, second ed., 1992.
14. K. M. Hanson and G. S. Cunningham, "A computational approach to Bayesian inference," in Computing Science and Statistics, 27, M. M. Meyer and J. L. Rosenberger, eds., pp. 202-211, Interface Foundation, Fairfax Station, VA 22039-7460, 1996.
15. W. Briggs, A Multigrid Tutorial, Society for Industrial and Applied Mathematics, Philadelphia, 1987.
16. P. J. Green, "Bayesian reconstruction from emission tomography data using a modified EM algorithm," IEEE Trans. on Medical Imaging 9(1), pp. 84-93, 1990.
17. R. Stevenson and E. Delp, "Fitting curves with discontinuities, " Proc. of the First International Workshop on Robust Computer Vision , pp. 127-136, (Seattle, WA), 1990.
18. K. Lange, "Convergence of EM image reconstruction algorithms with Gibbs smoothing," IEEE Trans. on Medical Imaging 9(4), pp. 439-446, 1990.
19. S. S. Saquib, C. A. Bouman, and K. Sauer, "ML parameter estimation for Markov random fields, with applications to Bayesian tomography," Tech. Rep. TR-ECE 95-24, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, 1995.
20. S. S. Saquib, C. A. Bouman, and K. Sauer, "A non-homogeneous MRF model for multiresolution Bayesian estimation," Proc. of IEEE Int'l Conf. on Image Proc. 2, pp. 445-448, (Lausanne, Switzerland), 1996.