Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications.The absorption(μa)and s...Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications.The absorption(μa)and scattering(μs)coe±cients of biological tissues are inversely analyzed from their diffuse re°ectance(R)and total transmittance(T),which are measured using a double integrating spheres(DIS)system.The inversion algorithms,for example,inverse adding doubling method and inverse Monte Carlo method,are sensitive to noise signals during the DIS measurements,resulting in reduced accuracy during determination.In this study,we propose an arti ficial neural network(ANN)to estimateμa andμs at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties.Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprisingμa,μs,R and T.Measurement noise signals were added to R and T,and the ANN model was then trained using the noise-added datasets.Numerical results showed that the trained ANN model reduced the effects of noise inμa andμs estimation.Experimental veri fication indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average,resulting in measurement time reduction.The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times,thus reducing changes in the optical properties due to desiccation of the samples.展开更多
基金supported by the Japan Society for the Promotion of Science KAKENHI(Grant numbers:20H04549 and 19K12822)the Japan Science and Technology Agency ACT–X(Grant Number:JPMJAX21K7).
文摘Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications.The absorption(μa)and scattering(μs)coe±cients of biological tissues are inversely analyzed from their diffuse re°ectance(R)and total transmittance(T),which are measured using a double integrating spheres(DIS)system.The inversion algorithms,for example,inverse adding doubling method and inverse Monte Carlo method,are sensitive to noise signals during the DIS measurements,resulting in reduced accuracy during determination.In this study,we propose an arti ficial neural network(ANN)to estimateμa andμs at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties.Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprisingμa,μs,R and T.Measurement noise signals were added to R and T,and the ANN model was then trained using the noise-added datasets.Numerical results showed that the trained ANN model reduced the effects of noise inμa andμs estimation.Experimental veri fication indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average,resulting in measurement time reduction.The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times,thus reducing changes in the optical properties due to desiccation of the samples.
