Performance Analysis of Reconstruction Algorithms in Diffuse Optical Tomography
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32769
Performance Analysis of Reconstruction Algorithms in Diffuse Optical Tomography

Authors: K. Uma Maheswari, S. Sathiyamoorthy, G. Lakshmi

Abstract:

Diffuse Optical Tomography (DOT) is a non-invasive imaging modality used in clinical diagnosis for earlier detection of carcinoma cells in brain tissue. It is a form of optical tomography which produces gives the reconstructed image of a human soft tissue with by using near-infra-red light. It comprises of two steps called forward model and inverse model. The forward model provides the light propagation in a biological medium. The inverse model uses the scattered light to collect the optical parameters of human tissue. DOT suffers from severe ill-posedness due to its incomplete measurement data. So the accurate analysis of this modality is very complicated. To overcome this problem, optical properties of the soft tissue such as absorption coefficient, scattering coefficient, optical flux are processed by the standard regularization technique called Levenberg - Marquardt regularization. The reconstruction algorithms such as Split Bregman and Gradient projection for sparse reconstruction (GPSR) methods are used to reconstruct the image of a human soft tissue for tumour detection. Among these algorithms, Split Bregman method provides better performance than GPSR algorithm. The parameters such as signal to noise ratio (SNR), contrast to noise ratio (CNR), relative error (RE) and CPU time for reconstructing images are analyzed to get a better performance.

Keywords: Diffuse optical tomography, ill-posedness, Levenberg Marquardt method, Split Bregman, the Gradient projection for sparse reconstruction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1123757

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1552

References:


[1] S. R. Arridge, “Optical tomography in medical imaging,” Inverse Problems, vol. 15, no. 2, pp. R41–R93, 1999.
[2] S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Problems, Vol. 25, no. 12, Article ID 123010, 2009.
[3] A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Physics in Medicine and Biology, Vol. 50, no. 4, article R1, 2005.
[4] Samir Kumar Biwas, Rajan Kanhirodan, Ram Mohan Vasu,” Models and algorithms for Diffuse Optical Tomographic System”, Network and system sciences, 6,489-496,2013
[5] Bo Bi, Bo Han, Weimin Han, Jinping Tang and Li Li, “Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation” computational and mathematical methods in Medicine, volume 2015, Article ID 286161,2015
[6] J. Nocedal and S. J. Wright, Numerical Optimization, Springer Series in Operations Research, Springer, New York, NY, USA, 1999.
[7] M. Hanke, “The regularizing Levenberg-Marquardt scheme is of optimal order,” Journal of Integral Equations and Applications, Vol. 22, no. 2, pp. 259–283, 2010.
[8] M. Gehre, T. Kluth, A. Lipponen et al., “Sparsity reconstruction in electrical impedance tomography: an experimental evaluation”, Journal of Computational and Applied Mathematics, Vol. 236, no. 8, pp. 2126–2136, 2012.
[9] J. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems, Springer, New York, NY, USA, 2005.
[10] D. L. Colton and R. Kress, Inverse Acoustic and Electromagnetic Scattering Theory, Springer, Berlin, Germany, 2nd edition, 1998.
[11] M. Jiang, T. Zhou, J. Cheng, W. X. Cong, and G. Wang, “Image reconstruction for bioluminescence tomography from partial measurement,” Optics Express, vol. 15, no. 18, pp. 11095–11116,2007.
[12] H. B. Jiang, Diffuse Optical Tomography: Principles and Applications, CRC Press, Boca Raton, Fla, USA, 1st edition, 2010.
[13] W. Han, J. A. Eichholz, X. L. Cheng, and G. Wang, “A theoretical framework of x-ray dark-field tomography,” SIAM Journal on Applied Mathematics, vol. 71, no. 5, pp. 1557–1577, 2011.
[14] H. Gao, S. Osher, and H. K. Zhao, “Quantitative photo acoustic tomography,” in Mathematical Modeling in Biomedical Imaging II, Lecture Notes in Mathematics, pp. 131–158, Springer, Berlin, Germany, 2012.
[15] J. Chamorro-servent, J. F. P. J. Abascal, J. Aguirre, Simon Arridge, Teresa Correia, Jorge Ripoll, Manuel Desco and Juan J. Vaquero, “Use of split Bergman denoising for iterative reconstruction in fluorescence diffuse optical tomography”, Journal of Biomedical optics, vol.18,no.7, Article ID 076016, 2013.
[16] J. Tang, W. Han, and B. Han, “A theoretical study for RTE-based parameter identification problems,” Inverse Problems, vol. 29, no. 9, Article ID095002, 2013.
[17] H. Gao and H. K. Zhao, “Analysis of a numerical solver for radiative transport equation, “Mathematics of Computation, Vol. 82, no. 281, pp. 153–172, 2013.
[18] R. Sukanyadevi, K. Umamaheswari, S. Sathiyamoorthy, Resolution improvement in Diffuse Optical Tomography, International Journal of Computer Applications ICIIIOES (9), 2013.
[19] K. Uma Maheswari, S. Sathiyamoorthy, “Soft Tissue Optical Property Extraction for Carcinoma Cell Detection in Diffuse Optical Tomography System under Boundary Element Condition”, Optik-International journal for light and electron optics, Vol. 127 (3), 1281-1290, 2016.
[20] K. Uma Maheswari, S. Sathiyamoorthy, “Stein’s Unbiased Risk Estimate Regularization (SURE) for Diffuse Optical Tomography (DOT) System Enhances Image Reconstruction with High Contrast to Noise Ratio (CNR)”, International Journal of Applied Engineering Research, 10 (24), 21186-21191, 2015.
[21] Sabrina brigade, Samuel Powell, Robert J cooper et al., “Evaluating real-time image reconstruction in diffuse optical tomography using physiologically realistic test data”, Biomedical optical express, Vol. 6, Issue 12, pp. 4719-4737 ,2015.
[22] J. F. CAI, S. Osher, and Z. W. Shen, “Linearized Bregman iterations for compressed sensing”, Mathematics of computation, vol. 78, no. 267, pp. 1515-1536,2009.
[23] T. Goldstein and S. Osher, “The Split Bregman method for L1 regularized problems”, SIAM journal on imaging sciences, Vol. 2, no. 2, pp. 323-343, 2009.
[24] W. Tyin, S. Osher, J. Durban, and D. Goldfarb, “Bregman iterative algorithms for l1 minimization with applications to compressed sensing”, SIAM journal on imaging science, Vol. 1, no. 1, pp. 143-168, 2008.
[25] W. T. Yin, “Analysis and generalization of the linearized Bregman model,” SIAM Journal on imaging science, vol. 3, no. 4, pp. 856-877, 2010.
[26] J. Bush, “Bregman algorithms”, M. S. thesis, University of California, Santa Barbara, Calif, USA, 2011.
[27] J. Wang, J. Ma, B. Han, and Q. Li, “Split Bregman iterative algorithm for sparse reconstruction of electrical impedance tomography”, Signal Processing, Vol. 92, no. 12, pp. 2952-2961, 2012.
[28] M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems”, IEEE journal of selected topics in Signal Processing, Vol. 1, no. 4, pp. 586-597, 2007.
[29] K.Umamaheswari, S.Sathiyamoorthy and G.Lakshmi, “Numerical solution for image reconstruction in diffuse optical tomography”, Journal of Engineering and Applied Science, vol.11, pp. 1332-1336.