Iterative image reconstruction algorithms based on cross-entropy minimization

Byrne, Charles

doi:10.1109/83.210869

Cited by 210 publications

(200 citation statements)

References 33 publications

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“…At each iteration, the Tikhonov regularisation is applied. The method was demonstrated to yield diffuse fluorescence tomograms of higher quality than the algebraic techniques ART and SMART [244], as well as the algorithm of non-negative least squares NNLS [239]. Thus, the studies performed at the experimental diffuse fluorescence tomograph designed in the IAP RAS confirmed the possibility of reliable resolution of two fluorescent inhomogeneities 3 mm in diameter, the separation between their centres being 6 mm.…”

Section: Perturbation Semianalytic Methodsmentioning

confidence: 55%

“…Thus, the resolution in DOT is a spatially varying quantity. ones [5,10,12,21,57,80,83,84,93,98,104,106,110,117,159,227,228,243,244]. However, to the opinion of the authors, the semianalytic methods of time-domain DOT have a perspective from the point of view of both improving the reconstruction accuracy and minimisation of processing time.…”

Section:  mentioning

confidence: 93%

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Diffuse optical mammotomography: state-of-the-art and prospects

Konovalov

Genina

Bashkatov

2016

JBPE

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Abstract:The principles of diffuse optical tomography (DOT) of tissues are presented. The DOT capabilities as a method of breast cancer diagnostics are analysed. The state-ofthe-art of the DOT instrumentation and methodological base in application to solving the mammography problems are described. The significant contribution of Russian scientists to the development of the DOT methodology is emphasised. Basing on the results of the analysis, the authors expect the possibility of soonest entry of diffuse optical mammotomographs to the market of medical imaging instrumentation, and the capability of Russian researchers to take part in the competition for this market. © 2016 Journal of Biomedical Photonics & Engineering.Key words: diffuse optical tomography, breast, optical and functional parameters, methods of determining optical parameters, methods of image reconstruction, perturbation model of reconstruction, temporal point spread function, spatial resolution, reconstruction time. University Press, Cambridge (2016). 7. S. R. Arridge, and M. Schweiger, "Inverse methods for optical tomography," in Information Processing in Medical Imaging, H.H. Barrett (ed.), Springer-Verlag, Flagstaff, 259-277 (1993). 8. J. C. Hebden, S. R. Arridge, and D. T. Delpy, "Optical imaging in medicine: I. Experimental techniques,"Phys. Med. Biol. 42, 825-840 (1997). 9. S. R. Arridge, and J. C. Hebden, "Optical imaging in medicine: II. Modelling and reconstruction," Phys. Med. Biol. 42, 841-853 (1997). 10. B. B. Das, F. Liu, and R. R. Alfano, "Time-resolved fluorescence and photon migration studies in biomedical and random media," Rep. Prog. Phys. 60, 227-292 (1997). 11. K. Michielsen, H. De'Raedt, J. Przeslawski, and N. Garcia, "Computer simulation of time-resolved optical imaging of objects hidden in turbid media," Phys. Rep. 304, 89-144 (1998). 12. S. R. Arridge, "Optical tomography in medical imaging," Inverse Problems 15, R41-R93 (1999). Darzi, "Diffuse optical imaging of the healthy and diseased breast: a systematic review," Breast Cancer Research and Treatment 108, 9-22 (2008). 19. S. L. Jacques, and B. W. Pogue, "Tutorial on diffuse light transport," J. Biomed. Opt. 13, 041302 (2008 Biol. 361, 171-179 (1994). 31. A. Yodh, and B. Chance, "Spectroscopy and imaging with diffusing light," Phys. Today 48, 34-40 (1995). 32. J. C. Hebden, S. R. Arridge, "Imaging through scattering media by the use of an analytical model of perturbation amplitudes in the time domain," Appl. Opt. 35, 6788-6796 (1996). 33. K. D. Paulsen, and H. Jiang, "Enhanced frequency-domain optical image reconstruction in tissues through total-variation minimization," Appl. Opt. 35, 3447-3458 (1996). 34. R. J. Grable, "Optical tomography improves mammography," Laser Focus World 32, 113-118 (1996). 35. V. V. Lyubimov, "The physical foundation of the strongly scattering media laser tomography," Proc. SPIE 2769SPIE , 107-110 (1996. 36. V. V. Lyubimov, "Optical tomography of highly scattering media by using the first transmitted photons of ultrashort pulses," Optics a...

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Section: Perturbation Semianalytic Methodsmentioning

confidence: 55%

Section:  mentioning

confidence: 93%

Diffuse optical mammotomography: state-of-the-art and prospects

Konovalov

Genina

Bashkatov

2016

JBPE

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“…Upon further testing, the MLEM estimator [23] proved to yield similar results at high count rates, and more accurate results at lower count rates. MLEM requires the implementation of an iterative algorithm:…”

Section: Reconstruction Resultsmentioning

confidence: 97%

Coded-Aperture Compton Camera for Gamma-Ray Imaging

Farber

2016

EPJ Web of Conferences

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Abstract.A novel gamma-ray imaging system is demonstrated, by means of Monte Carlo simulation. Previous designs have used either a coded aperture or Compton scattering system to image a gamma-ray source. By taking advantage of characteristics of each of these systems a new design can be implemented that does not require a pixelated stopping detector. Use of the system is illustrated for a simulated radiation survey in a decontamination and decommissioning operation.

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“…In case , which is the more realistic one, the convergence proof is more complicated and only special cases have been considered. (See, for instance, [16], [17], [34], and [7], where the case of positivity constraints was discussed.) Our present case, involving the constraint set (4), may be settled using the same type of reasoning.…”

Section: Alternating Projectionsmentioning

confidence: 99%

“…Observe that in the dynamic case the M-step is intrinsically more complicated than in the stationary case, because it requires solving a nonlinear optimization problem ML where the static case goes with an explicit formula, (cf. [17], [7]). We therefore have to assure that a reasonably fast numerical solution is possible.…”

Section: Numerical Approachmentioning

confidence: 99%

An EM algorithm for dynamic SPECT

Bauschke

Noll

Celler

et al. 1999

IEEE Trans. Med. Imaging

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Abstract-In this paper we present two variants of the EM algorithm for dynamic SPECT imaging. A version based on compartmental modeling which fits a sum of exponentials and a more general approach allowing for arbitrary decaying activities. The underlying probabilistic models are discussed and the incomplete and complete data spaces are shown to be physically meaningful.We indicate that the second method, leading to a convex program in the M step, is easier to treat numerically and we present a possible numerical approach. Some preliminary numerical tests indicating the feasibility of the method are included.

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Iterative image reconstruction algorithms based on cross-entropy minimization

Cited by 210 publications

References 33 publications

Diffuse optical mammotomography: state-of-the-art and prospects

Diffuse optical mammotomography: state-of-the-art and prospects

Coded-Aperture Compton Camera for Gamma-Ray Imaging

An EM algorithm for dynamic SPECT

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