Particle distribution estimation is an important issue in medical diagnosis. In particular, photon scattering in some medical devices extremely degrades image quality and causes measurement inaccuracy. The Monte Carlo (MC) algorithm is regarded as the most accurate particle estimation approach but is still time-consuming, even with graphic processing unit (GPU) acceleration. The goal of this work is to develop an automatic scatter estimation framework for high-efficiency photon distribution estimation. Specifically, a GPU-based MC simulation initially yields a raw scatter signal with a low photon number to hasten scatter generation. In the proposed method, assume that the scatter signal follows Poisson distribution, where an optimization objective function fused with sparse feature penalty is modeled. Then, an over-relaxation algorithm is deduced mathematically to solve this objective function. For optimizing the parameters in the over-relaxation algorithm, the deep $Q$-network in the deep reinforcement learning scheme is built to intelligently interact with the over-relaxation algorithm to accurately and rapidly estimate a scatter signal with the large range of photon numbers. Experimental results demonstrated that our proposed framework can achieve superior performance with structural similarity $>0.94$, peak signal-to-noise ratio $>26.55\mathrm{dB}$, and relative absolute error $<5.62\%$, and the lowest computation time for one scatter image generation can be within 2 s.

中文翻译：

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蒙特卡洛模拟与目标分布建模相融合，通过深度强化学习进行自动高效光子分布估计

粒子分布估计是医学诊断中的重要问题。特别是，某些医疗设备中的光子散射会极大地降低图像质量，并导致测量不准确。蒙特卡洛（MC）算法被认为是最精确的粒子估计方法，但即使使用图形处理单元（GPU）加速，仍然很耗时。这项工作的目的是开发一种用于高效光子分布估计的自动散射估计框架。具体来说，基于GPU的MC仿真最初会产生光子数较低的原始散射信号，以加快散射的产生。在所提出的方法中，假设散射信号遵循泊松分布，其中对融合了稀疏特征代价的优化目标函数进行建模。然后，通过数学推导过松弛算法来解决该目标函数。为了优化过松弛算法中的参数，$问$深度强化学习方案中的神经网络被构建为与过松弛算法进行智能交互，以准确，快速地估计具有大范围光子数的散射信号。实验结果表明，我们提出的框架可以在结构相似的情况下实现出色的性能$>0.94$，峰值信噪比 $>26.55\mathrm{D\; b}$和相对绝对误差 $<5.62％$，并且一个散射图像生成的最低计算时间可以在2 s之内。