论文标题
通过动态机器学习对密集分层对象的限量角度层析成果重建
Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning
论文作者
论文摘要
强烈散射准透明物体的有限角度层析成像是一个具有挑战性的，严重的问题，对医学和生物成像，制造，自动化以及环境和粮食安全的实际意义。通过改善此类问题的状况来减少伪像，必须将先验的先验进行正规化。最近，有一种有效的方法是学习强烈散射但高度结构化的3D对象的有效方法，例如分层和曼哈顿是通过静态神经网络[Goy等人，Proc。纳特。学院。科学。 116，19848-19856（2019）]。在这里，我们提出了一种根本不同的方法，其中从多个角度的原始图像收集类似于由对象依赖的前向散射算子驱动的动态系统。照明角度的序列指数在动态系统类比中发挥离散时间的作用。因此，成像问题变成了非线性系统识别的问题，这也表明动态学习更适合使重建正规化。我们设计了一个复发性神经网络（RNN）结构，其新颖的分裂跨横向封闭式复发单元（SC-GRU）是基本的构建块。通过对几个定量指标的全面比较，我们表明，动态方法在先前的静态方法上有所改善，其伪影和更好的总体重建保真度。
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.
