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Proceedings Paper

A reduced-space basis function neural network method for diffuse optical tomography
Author(s): Hyun Keol Kim; Jacqueline Gunther; Jennifer Hoi; Andreas H. Hielscher
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Paper Abstract

We propose here a reduced space image reconstruction method that makes use of basis function neural network (BFNN) within a framework of PDE-constrained algorithm. This method reduces the solution space using the basis function approach, and finds the optimal solution through the learning process of neural network. The basis function approach improves the ill-posed nature of an original inverse problem, reducing the number of unknowns as well as regularizing the solution automatically. The proposed method was applied to breast cancer imaging, and the reconstruction performance was evaluated on how well the method can identify the tumor location in breast tissue. The results show that the BFNN method gives better results in the identification of tumor location than the traditional element-based reconstruction method.

Paper Details

Date Published: 5 March 2015
PDF: 6 pages
Proc. SPIE 9319, Optical Tomography and Spectroscopy of Tissue XI, 931925 (5 March 2015); doi: 10.1117/12.2080550
Show Author Affiliations
Hyun Keol Kim, Columbia Univ. (United States)
Jacqueline Gunther, Columbia Univ. (United States)
Jennifer Hoi, Columbia Univ. (United States)
Andreas H. Hielscher, Columbia 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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