基于相位的对齐与平行束断层扫描数据改进的投影匹配
Phase Based Alignment and Improved Projection Matching of Parallel Beam Tomography Data
AI总结：
本文提出了一种新的方法，将对齐问题视为傅里叶域中相位偏移的恢复问题，通过计算不准确数据与当前重建之间的差异来实现数据对齐，并展示了其灵活性和准确性。
摘要：
断层扫描是一种通过从成像域的线积分数据中重建场景的成像技术。重建过程中的基本假设是数据值的精确对齐，即数据值与积分路径之间的关系是已知的。在许多应用中，例如电子和X射线断层扫描，由于旋转物理样品时的偏移，需要使用软件对齐技术或图像配准来建立这种关系。不幸的是，高精度的软件对齐在许多情况下仍然具有挑战性，不正确的对齐会导致成像分辨率的严重损失。本文开发了一种新方法，将对齐问题通过完全不同的视角视为重建算法中的傅里叶域相位偏移恢复问题。这些相位偏移的恢复作为数据对齐，通过计算不准确数据与当前重建之间的差异来实现。在方法开发过程中，我们研究了参数的适当选择，并展示了其相当灵活且出人意料的准确性。最后，我们的分析提供了关于通过低通滤波改进投影匹配对齐的见解，我们进行了演示。我们的方法在广泛的应用示例和设置中得到了验证。
英文摘要：
Tomography is an imaging technique that works by reconstructing a scene from acquired data in the form of line integrals of the imaging domain. A fundamental underlying assumption in the reconstruction procedure is the precise alignment of the data values, i.e. the relationship between the data values and the paths of the lines of integration is accurately known. In many applications, e.g. electron and X-ray tomography, it is necessary to establish this relationship using software alignment techniques or image registration due to misalignment when rotating the physical specimen. Unfortunately, highly accurate software alignment is still a challenge to achieve in many cases, and improper alignment results in severe loss in the imaging resolution. In this article, we develop a new approach that considers the alignment problem through a completely different lens, as a problem of recovering phase shifts in Fourier domain {within} the reconstruction algorithm. The recovery of these phase shifts serves as the data alignment, which is done by calculating discrepancies between the misaligned data and the current reconstruction. In the development of the approach, we investigate proper selection of parameters, and we show that it is fairly flexible and surprisingly accurate. Finally, the analysis of our approach provides insight into why projection matching alignment by cross-correlation can be improved through low pass filtering, which we demonstrate. Our methods are validated in a wide range of examples and settings.
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。
