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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