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Proceedings Paper

3D parameter reconstruction in hyperspectral diffuse optical tomography
Author(s): Arvind K. Saibaba; Nishanth Krishnamurthy; Pamela G. Anderson; Jana M. Kainerstorfer; Angelo Sassaroli; Eric L. Miller; Sergio Fantini; Misha E. Kilmer
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Paper 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.

Paper Details

Date Published: 5 March 2015
PDF: 5 pages
Proc. SPIE 9319, Optical Tomography and Spectroscopy of Tissue XI, 93191B (5 March 2015); doi: 10.1117/12.2079775
Show Author Affiliations
Arvind K. Saibaba, Tufts Univ. (United States)
Nishanth Krishnamurthy, Tufts Univ. (United States)
Pamela G. Anderson, Tufts Univ. (United States)
Jana M. Kainerstorfer, Tufts Univ. (United States)
Angelo Sassaroli, Tufts Univ. (United States)
Eric L. Miller, Tufts Univ. (United States)
Sergio Fantini, Tufts Univ. (United States)
Misha E. Kilmer, Tufts Univ. (United States)


Published in SPIE Proceedings Vol. 9319:
Optical Tomography and Spectroscopy of Tissue XI
Bruce J. Tromberg; Arjun G. Yodh; Eva Marie Sevick-Muraca; Robert R. Alfano, Editor(s)

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