Computation of the optical properties of tissues from light reflectance using a neural network
We have established a neural network to quickly deduce optical properties of tissue slabs from the diffuse reflectance distribution. Diffusion theory based on multiple image sources mirrored about the two extrapolated boundaries is used to prepare the training and testing sets for the neural network. The neural network is trained using backpropagation with the conjugate gradient method. Once the neural network is trained, it is able to deduce optical properties of tissues within on the order of a millisecond. The range of the tissue optical properties that is covered by our neural network is 0.01 - 2 cm^(-1) for absorption coefficient, 5 - 25 cm^(-1) for reduced scattering coefficient, and 0.001 - 1 cm for tissue thickness. A separate network is also trained for thick tissue slabs. A simple experimental setup applying the trained neural network is designed to measure tissue optical properties quickly.
© 1994 Society of Photo-Optical Instrumentation Engineers (SPIE). We thank Linda Eppich for proofreading the manuscript. This research was supported in part by the Office of Naval Research (N00014-91-J-1354), the Air Force Office of Scientific Research (F49620-93-1-0298DEF), and the National Institutes of Health (R29-HL45045).
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