论文标题
使用蒙特卡洛辐射传输模型估算光学性质的有效反转策略
Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models
论文作者
论文摘要
生物医学光学器件中的间接成像问题通常需要重复评估辐射传输模型，蒙特卡洛（Monte Carlo）准确但计算上的昂贵。我们开发了一种新的方法来减少这种瓶颈，该方法对各种医疗和工业应用中的定量层析成像具有重要意义。使用蒙特卡洛，我们为给定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，我们随后在整个迭代反转方案中自适应地控制了该梯度的准确性，以便在每个步骤中大大减少计算资源。例如，定量光声断层扫描和超声调制的光学断层扫描问题，我们证明了使用总计算费用可与（或小于）相同的（或小于）同一蒙特卡洛模型的高精度前进所需的总计算费用来实现。当使用随机方法接近光学性质估计的完整非线性反问题时，这种方法表明了大量的计算节省。
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 a novel approach to reduce this bottleneck which has significant implications for quantitative tomographic imaging in a variety of medical and industrial applications. 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, in order to substantially reduce computational resources at each step. 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. This approach demonstrates significant computational savings when approaching the full non-linear inverse problem of optical property estimation using stochastic methods.
