24 October 2016 Sampling scheme optimization for diffuse optical tomography based on data and image space rankings
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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)
Sohail Sabir, Sohail Sabir, Changhwan Kim, Changhwan Kim, Sanghoon Cho, Sanghoon Cho, Duchang Heo, Duchang Heo, Kee Hyun Kim, Kee Hyun Kim, Jong Chul Ye, Jong Chul Ye, Seungryong Cho, 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 . Submission:

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