24 May 2018 A dimension reduction method for fast diffuse optical tomography
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Abstract
Because the inverse problem in diffuse optical tomography (DOT) is highly ill-posed in general, appropriate regularization based on prior knowledge of the target is necessary for the reconstruction of the image. The total variation L1 norm regularization method (TV-L1) that preserves the boundaries of a target is known to have excellent result in image reconstruction. However, large computational cost of the TV-L1 prevents its use in portable applications. In this study, we propose a dimension reduction method in DOT for fast and hardware-efficient image reconstruction. The proposed method is based on the fact that the optical flux from a light source in a highly scattering medium is localized spatially. As such, the dimension of a sensitivity matrix used in the forward model of the DOT can be reduced by eliminating uncorrelated subspaces. The simulation results indicate up to 96.1% reduction in dimensions and up to 79.3% reduction in runtime while suppressing the reconstruction error below 2.26%.
Conference Presentation
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Mingyu Choi, Mingyu Choi, Jaemyoung Kim, Jaemyoung Kim, Minsu Ji, Minsu Ji, Haeil Lee, Haeil Lee, Sungkwon Yu, Sungkwon Yu, Hyeon-min Bae, Hyeon-min Bae, } "A dimension reduction method for fast diffuse optical tomography", Proc. SPIE 10677, Unconventional Optical Imaging, 106770Y (24 May 2018); doi: 10.1117/12.2305691; https://doi.org/10.1117/12.2305691
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