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Experimental investigation of perturbation Monte-Carlo based derivative estimation for imaging low-scattering tissue

Phaneendra K. Yalavarthy, Kirtish Karlekar, H. S. Patel, R. M. Vasu, Manojit Pramanik, P. C. Mathias, B. Jain, and P. K. Gupta

Open Access Open Access

Abstract

Experimental results for imaging the low-scattering tissue phantoms based on the derivative estimation through perturbation Monte-Carlo (pMC) method are presented. It is proven that pMC-based methods give superior reconstructions compared to diffusion-based reconstruction methods. An easy way to estimate the Jacobian using analytical expression obtained from perturbation Monte-Carlo method is employed. Simulation studies on the same objects, considered in the experiment, are performed and corresponding results are found to be in reasonable agreement with the experimental studies. It is shown that inter-parameter cross talk in diffusion based methods lead to false results for the low-scattering tissue, where as the pMC-based method gives accurate results.

©2005 Optical Society of America

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Figures (10)
Fig. 1.
Fig. 1. The flowchart used in the iterative reconstruction. The inner loop uses a gradient search algorithm to output update vectors for the optical properties.

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Fig. 2.
Fig. 2. Schematic diagram of the time-domain experimental setup.

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Fig. 3.
Fig. 3. Reconstruction of µa-distribution obtained from PBP approach with experimental data from tissue-equivalent phantom with only µa-inhomogeneity (a). Original µa-distribution: Background µab and µsb are 0.008 mm–1 and 0.05 mm-1 respectively, and the inclusion has µain=0.021 mm-1 and µsin=0.05 mm-1. (b). Reconstruction of (a). The reconstructed µainhomogeneity value at its centre is µain=0.028 mm-1.

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Fig. 4.
Fig. 4. Reconstruction of µs-distribution obtained from PBP approach with experimental data from tissue-equivalent phantom with only µs-inhomogeneity (a). Original µa-distribution: Background µab and µsb are 0.08 mm–1 and 0.05 mm-1 respectively, and the inclusion has µain=0.08 mm-1 and µsin=0.14 mm-1. (b). Reconstruction of (a). The reconstructed µsinhomogeneity value at its centre is µsin=0.18 mm -1.

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Fig. 5.
Fig. 5. Simultaneous reconstruction of µa and µs inclusions from the experimental data obtained from the composite phantom using the PBP approach (a). Original µa distribution with background µab=0.008 mm-1 µsb=0.05 mm-1 and inclusion has µina=0.021 mm-1 (b). Original µs distribution: background is same as (a) and the inclusion µsin=0.14 mm-1. Reconstruction of (c). µa-inhomogeneity and (d). µs-inhomogeneity. The reconstructed optical properties at centers of inhomogeneities are µain=0.028 mm-1 and µsin=0.21 mm-1.

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Fig. 6.
Fig. 6. Simulation results for Fig. 3(a). (a). Reconstructed µa-image with a priori information about the location of the inhomogeneity (0.018 mm-1 at the centre). (b). Reconstructed image without a priori information about the location of the inhomogeneity (0.019 mm-1 at the centre).

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Fig. 7.
Fig. 7. Simulation result for Fig. 4(a). Reconstructed µs-image with a priori information about the location of the inhomogeneity (0.16 mm-1 at the centre).

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Fig. 8.
Fig. 8. Simulation results for Fig. 5(a) & (b). (a). Reconstructed µa-image with a priori information about the location of the inhomogeneity (0.019 mm-1 at the centre). (b). Reconstructed µs-image without a priori information about the location of the inhomogeneity (0.13 mm -1 at the centre).

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Fig. 9.
Fig. 9. Comparison of horizontal cross-sections at y=41 mm of the reconstructed images.

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Fig. 10.
Fig. 10. Diffusion equation based reconstruction results for Fig. 5(a) and (b) from the simulated data without noise. (a). Reconstructed µa-image (0.011 mm -1 at the centre). (b). Reconstructed µs-image (0.06 mm -1 at the centre).

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Equations (1)

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w ̅ = w ( μ ̅ s μ ̅ t μ s μ t ) n ( μ ̅ t μ t ) n exp [ − ( μ ̅ t − μ t ) l ]
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