Estimation of the optical properties of scattering media such as tissue is
important in diagnostics as well as in the development of techniques to image
deeper. As light penetrates the sample scattering events occur that alter the
propagation direction of the photons in a random manner leading degradation of
image quality. The distribution of the scattered light does, however, give a
measure of the optical properties such as the reduced scattering coefficient
and the absorption coefficient. Unfortunately, inverting scattering patterns to
recover the optical properties is not simple, especially in the regime where
the light is partially randomized. Machine learning has been proposed by
several authors as a means of recovering these properties from either the back
scattered or the transmitted light. In the present paper, we train a general
purpose convolutional neural network RESNET 50 with simulated data based on
Monte Carlo simulations. We show that compared with previous work our approach
gives comparable or better reconstruction accuracy with training on a much
smaller dataset. Moreover, by training on multiple parameters such as the
intensity distribution at multiple planes or the exit angle and spatial
distribution one achieves improved performance compared to training on a single
input such as the intensity distribution captured at the sample surface. While
our approach gives good parameter reconstruction, we identify factors that
limit the accuracy of the recovered properties, particularly the absorption
coefficient. In the light of these limitations, we suggest how the present
approach may be enhanced for even better performance.

通过使用卷积神经网络 RESNET 50 和模拟数据，我们训练的通用卷积神经网络能够从散射介质中恢复光学特性的参数，具有较小数据集的训练相比之前的研究具有相媲美或更好的重建准确性。此外，通过在多个参数上进行训练，如多个平面上的强度分布或出射角度和空间分布，与仅基于采样表面强度分布进行训练相比，我们实现了更好的性能。尽管我们的方法能够进行良好的参数重建，但我们还指出了限制恢复特性精度的因素，尤其是吸收系数。基于这些限制，我们提出了如何进一步增强当前方法以获得更好性能的建议。