Significance: Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop an approach to reduce this bottleneck, which has significant implications for quantitative tomographic imaging in a variety of medical and industrial applications. Aim: Our aim is to enable computationally efficient image reconstruction in (hybrid) diffuse optical modalities using stochastic forward models. Approach: Using Monte Carlo, we compute a fully stochastic gradient of an objective function for a given imaging problem. Leveraging techniques from the machine learning community, we then adaptively control the accuracy of this gradient throughout the iterative inversion scheme to substantially reduce computational resources at each step. Results: For example problems of quantitative photoacoustic tomography and ultrasound-modulated optical tomography, we demonstrate that solutions are attainable using a total computational expense that is comparable to (or less than) that which is required for a single high-accuracy forward run of the same Monte Carlo model. Conclusions: This approach demonstrates significant computational savings when approaching the full nonlinear inverse problem of optical property estimation using stochastic methods.

中文翻译：

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用蒙特卡罗辐射传输模型估计光学特性的有效反演策略。

意义：生物医学光学中的间接成像问题通常需要对辐射传输的前向模型进行重复评估，蒙特卡罗对此模型准确但计算成本高。我们开发了一种减少这种瓶颈的方法，这对各种医学和工业应用中的定量断层成像具有重要意义。目标：我们的目标是使用随机前向模型在（混合）漫反射光学模式中实现计算高效的图像重建。方法：使用蒙特卡罗，我们为给定的成像问题计算目标函数的完全随机梯度。利用机器学习社区的技术，然后，我们在整个迭代反演方案中自适应地控制此梯度的准确性，以显着减少每一步的计算资源。结果：例如定量光声断层扫描和超声调制光学断层扫描的问题，我们证明使用与单个高精度前向运行所需的总计算费用相当（或低于）的总计算费用可以获得解决方案。相同的蒙特卡罗模型。结论：当使用随机方法处理光学特性估计的完全非线性逆问题时，该方法显示出显着的计算节省。我们证明了使用与同一蒙特卡罗模型的单个高精度前向运行所需的总计算费用相当（或低于）的总计算费用可以获得解决方案。结论：当使用随机方法处理光学特性估计的完全非线性逆问题时，该方法显示出显着的计算节省。我们证明了使用与同一蒙特卡罗模型的单个高精度前向运行所需的总计算费用相当（或低于）的总计算费用可以获得解决方案。结论：当使用随机方法处理光学特性估计的完全非线性逆问题时，该方法显示出显着的计算节省。