基于动态机器学习的稠密分层物体有限角度层析重建
Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning
AI总结：
针对强散射准透明物体有限角度层析成像的不适定问题，提出将多角度成像类比为动态系统的思路，设计了以SC-GRU为核心的RNN架构，相比静态方法实现了更少伪影、更高保真度的重建。
摘要：
强散射准透明物体的有限角度层析成像是一个极具挑战性的高度不适定问题，在医学与生物成像、制造业、自动化以及环境与食品安全领域具有实际应用价值。为改善这类问题的条件以减少伪影，引入正则化先验是必要的。近期研究表明，针对强散射但具有高度结构化的三维物体（例如分层结构与曼哈顿结构），一种有效的先验学习方法是使用静态神经网络[Goy等人，《美国国家科学院院刊》116卷，19848-19856页（2019年）]。本文提出了一种截然不同的方法：将多角度原始图像的采集过程类比为一个由依赖于物体的前向散射算子驱动的动态系统，照明角度的序列索引在该动态系统类比中充当离散时间的角色。由此，成像问题转化为非线性系统辨识问题，这也表明动态学习更适合为重建过程提供正则化。我们设计了一种循环神经网络（RNN）架构，其核心构建模块是一种新型的分裂卷积门控循环单元（SC-GRU）。通过多个量化指标的综合对比，我们证明该动态方法优于以往的静态方法，伪影更少，整体重建保真度更高。
英文摘要：
Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem with practical implications in medical and biological imaging, manufacturing, automation, and environmental and food security. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. Recently, it was shown that one effective way to learn the priors for strongly scattering yet highly structured 3D objects, e.g. layered and Manhattan, is by a static neural network [Goy et al, Proc. Natl. Acad. Sci. 116, 19848-19856 (2019)]. Here, we present a radically different approach where the collection of raw images from multiple angles is viewed analogously to a dynamical system driven by the object-dependent forward scattering operator. The sequence index in angle of illumination plays the role of discrete time in the dynamical system analogy. Thus, the imaging problem turns into a problem of nonlinear system identification, which also suggests dynamical learning as better fit to regularize the reconstructions. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the fundamental building block. Through comprehensive comparison of several quantitative metrics, we show that the dynamic method improves upon previous static approaches with fewer artifacts and better overall reconstruction fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
