3D parameter reconstruction in hyperspectral diffuse optical tomography

Arvind K. Saibaba; Nishanth Krishnamurthy; Pamela G. Anderson; Jana M. Kainerstorfer; Angelo Sassaroli; Eric L. Miller; Sergio Fantini; Misha E. Kilmer

doi:10.1117/12.2079775

5 March 2015 3D parameter reconstruction in hyperspectral diffuse optical tomography

Arvind K. Saibaba, Nishanth Krishnamurthy, Pamela G. Anderson, Jana M. Kainerstorfer, Angelo Sassaroli, Eric L. Miller, Sergio Fantini, Misha E. Kilmer

Author Affiliations +

Proceedings Volume 9319, Optical Tomography and Spectroscopy of Tissue XI; 93191B (2015) https://doi.org/10.1117/12.2079775
Event: SPIE BiOS, 2015, San Francisco, California, United States

Abstract

The imaging of shape perturbation and chromophore concentration using Diffuse Optical Tomography (DOT) data can be mathematically described as an ill-posed and non-linear inverse problem. The reconstruction algorithm for hyperspectral data using a linearized Born model is prohibitively expensive, both in terms of computation and memory. We model the shape of the perturbation using parametric level-set approach (PaLS). We discuss novel computational strategies for reducing the computational cost based on a Krylov subspace approach for parameteric linear systems and a compression strategy for the parameter-to-observation map. We will demonstrate the validity of our approach by comparison with experiments.

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Arvind K. Saibaba, Nishanth Krishnamurthy, Pamela G. Anderson, Jana M. Kainerstorfer, Angelo Sassaroli, Eric L. Miller, Sergio Fantini, and Misha E. Kilmer "3D parameter reconstruction in hyperspectral diffuse optical tomography", Proc. SPIE 9319, Optical Tomography and Spectroscopy of Tissue XI, 93191B (5 March 2015); https://doi.org/10.1117/12.2079775

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