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1 March 2019 Convolutional deep network for light propagation in heterogeneous bio-tissues
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Abstract
To calculate the light propagation through heterogeneous bio-tissues, we propose a convolutional deep network with specified convolutional kernels and calculation rules. The form of convolution kernels is chosen to be capable to imitate the absorption and scattering event by convolution operation. In the meanwhile, the multi kernel and masking mechanism we set provide the capability to calculate propagation in voxelized heterogeneous bio-tissue structures with pre-set tissue types and specific optical properties. The two-dimensional convolution operations are carried out multiple times until all the photons leaves the structure or absorbed by the tissues. Application of our network with kernels in the form of semi-infinite homogeneous radiative transfer equation (RTE) solution, and semi-infinite homogeneous diffusion equation (DE) solution are implemented to three artificial manipulated structures, including homogeneous phantom, two-layer structure and two-layer structure with a third tissue type inside layer two. The result comparing to Monte-Carlo simulation reveals the potential to form a new forward calculation model for diffuse optical tomography.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiang Fang and Ting Li "Convolutional deep network for light propagation in heterogeneous bio-tissues", Proc. SPIE 10876, Optical Interactions with Tissue and Cells XXX, 108760R (1 March 2019); doi: 10.1117/12.2507624; https://doi.org/10.1117/12.2507624
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