University of Eastern Finland, Department of Applied Physics, Kuopio, Finland.
University College London, Department of Computer Science, London, United Kingdom.
J Biomed Opt. 2022 Apr;27(8). doi: 10.1117/1.JBO.27.8.083013.
The image reconstruction problem in quantitative photoacoustic tomography (QPAT) is an ill-posed inverse problem. Monte Carlo method for light transport can be utilized in solving this image reconstruction problem.
The aim was to develop an adaptive image reconstruction method where the number of photon packets in Monte Carlo simulation is varied to achieve a sufficient accuracy with reduced computational burden.
The image reconstruction problem was formulated as a minimization problem. An adaptive stochastic Gauss-Newton (A-SGN) method combined with Monte Carlo method for light transport was developed. In the algorithm, the number of photon packets used on Gauss-Newton (GN) iteration was varied utilizing a so-called norm test.
The approach was evaluated with numerical simulations. With the proposed approach, the number of photon packets needed for solving the inverse problem was significantly smaller than in a conventional approach where the number of photon packets was fixed for each GN iteration.
The A-SGN method with a norm test can be utilized in QPAT to provide accurate and computationally efficient solutions.
定量光声断层扫描（QPAT）中的图像重建问题是一个不适定的反问题。光传输的蒙特卡罗方法可用于解决这个图像重建问题。
目的是开发一种自适应图像重建方法，其中蒙特卡罗模拟中的光子包数量会变化，以在降低计算负担的同时实现足够的精度。
将图像重建问题表述为最小化问题。开发了一种结合光传输的蒙特卡罗方法的自适应随机高斯牛顿（A-SGN）方法。在算法中，利用所谓的范数测试来改变高斯牛顿（GN）迭代中使用的光子包数量。
该方法通过数值模拟进行了评估。与传统方法相比，该方法在解决反问题时所需的光子包数量显著减少，在传统方法中，每个 GN 迭代的光子包数量都是固定的。
具有范数测试的 A-SGN 方法可用于 QPAT 中，以提供准确且计算高效的解决方案。
