Open Access
24 October 2016 Sampling scheme optimization for diffuse optical tomography based on data and image space rankings
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Abstract
We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source–detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2016/$25.00 © 2016 SPIE
Sohail Sabir, Changhwan Kim, Sanghoon Cho, Duchang Heo, Kee Hyun Kim, Jong Chul Ye, and Seungryong Cho "Sampling scheme optimization for diffuse optical tomography based on data and image space rankings," Journal of Biomedical Optics 21(10), 106004 (24 October 2016). https://doi.org/10.1117/1.JBO.21.10.106004
Published: 24 October 2016
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CITATIONS
Cited by 7 scholarly publications.
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KEYWORDS
Intelligence systems

Diffuse optical tomography

Sensors

Absorption

Scattering

Data modeling

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