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Sparse Image Reconstruction in Diffuse Optical Tomography: An Application of Compressed Sensing

M. Süzen, A. Giannoula, P. Zirak, N. Oliverio, U. Weigel, P. Farzam, and T. Durduran

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Abstract

In this paper we study the application of Compressed Sensing (CS) framework for optical tomography based on the Rytov approximation to the heterogeneous photon diffusion equation. Simulations are performed on a sample system to validate and compare inverse image reconstructions with l1-regularization (CS) and Singular Value Decomposition (SVD) respectively. Potential benefits and shortcomings of CS are discussed and are shown in the context of diffuse optical imaging.

© 2010 Optical Society of America

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