发表机构
Technische Universität München; Waygate Technologies, Baker Hughes Digital Solutions GmbH; Technische Universität Darmstadt; Munich Center for Machine Learning (MCML)（慕尼黑工业大学; 韦盖特技术，贝克休斯数字解决方案有限公司; 达姆施塔特工业大学; 慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对显微CT二次辐射导致成像不完整的问题，本文提出利用投影空间连续数学模型描述二次正弦图，并据此实现从有限角度观测中有效插值二次扫描的方法。
AI 中文摘要
计算机断层扫描（CT）已广泛应用于医学检查和无损检测。显微CT（微焦点X射线CT系统）是一种先进的版本，能够观察小物体的内部结构。然而，二次辐射可能阻止显微CT对物体完整结构进行成像。在二次扫描次数与主扫描次数相同的情况下，简单的减法可以解决问题，但代价是采集时间加倍。为了减少采集时间，目标是将二次扫描限制在尽可能少的成像角度，并通过插值恢复缺失数据。针对有限角度断层扫描，已有多种插值方法被探索，如多项式或样条插值、压缩感知、深度学习等。与许多这些场景不同，本文考虑的二次成像设置通常表现出简单的正弦结构，而上述任何方法都未直接利用这些结构。本文旨在通过一个仅利用投影空间信息的连续数学模型来描述二次正弦图，从而填补这一空白。基于该模型，我们提出了一种插值方法，能够从有限的观测中有效地插值二次扫描。
英文摘要
Computed tomography (CT) has been widely used in medical examinations and non-destructive testing. Micro-CT (microfocus X-ray CT system) is an advanced version that can observe the internal structures of small objects. However, secondary radiation can prevent micro-CT from imaging the full structure of the object. Given the same amount of secondary scans as primary scans, simple subtraction solves the problem at the price of doubled acquisition time. To reduce the acquisition time, one aims to limit the secondary scans to as few imaging angles as possible and interpolate to recover the missing data. Different interpolation methods have been explored for limited-angle tomography, such as polynomial or spline interpolation, compressive sensing, deep learning, etc. In contrast to many of these scenarios, the secondary imaging setup considered in this paper often exhibits simple sinusoidal structures that are not exploited directly by any of the aforementioned approaches. This paper aims to fill this gap with a continuous mathematical model describing the secondary sinogram only with information from the projection space. Building on this model, we propose an interpolation method that effectively interpolates secondary scans from limited observations.
