Computational Imaging in 3D X-Ray Microscopy: Reconstruction, Image Segmentation and Time-Evolved Experiments

Abstract

Time-dependent X-ray Microscopy (XRM) is an excellent technique to develop a fundamental mechanistic understanding of material behavior. Computational imaging plays a critical role in XRM, in a variety of ways. 2D projections are acquired and the resulting datasets are reconstructed using a filtered back projection algorithm. Several imaging artifacts are typically present, such as beam hardening, misalignment of the data, drift during time-evolved experiments (particularly at high temperatures and/or nanometer resolution scans), etc. Minimizing and removing these artifacts is, thus, very important. This is all the more important, because image segmentation is then done to quantify the statistics of the microstructure (often, as a function of time). The efficiency and accuracy of image segmentation is directly proportional to the quality of the initial reconstructed data. Thus, there is a need to develop efficient, robust algorithms that can handle large datasets obtained by 4D, time-dependent x-ray microscopy. In this paper, we will describe the challenges associated with computation imaging during x-ray microscopy. The use of Convolutional Neural Network (CNN) architectures based on a deep learning approach, as means of automating and handling x-ray microscopy data sets, in both lab-scale and synchrotron, will be discussed. The use of CNN techniques to robustly process ultra-large volumes of data in relatively small-time frames can exponentially accelerate tomographic data analysis, opening up novel avenues for performing 4D characterization experiments.

Calibration by differentiation – Self‐supervised calibration for X‐ray microscopy using a differentiable cone‐beam reconstruction operator

“…XRM is applied in a range of different research disciplines, among them are material science, geoscience and life science 18–21 . Depending on the application, the exact type of microscope can differ in a number of aspects, such as the source properties or the scanning procedure.…”

“…XRM is applied in a range of different research disciplines, among them are material science, geoscience and life science. [18][19][20][21] Depending on the application, the exact type of microscope can differ in a number of aspects, such as the source properties or the scanning procedure. The basic working principle of the system deployed in this study is similar to classical CBCT, but it deploys an additional optical system which further increases the resolution.…”

Real-time porosity mapping and visualization for synchrotron tomography

“…Achieving this process in real-time is complicated by the need both to automate the image processing steps and for code optimization as the data passes through multiple data structures (raw data, tomographic volume, collection of voids, polygonal mesh for visualization). Convolutional neural networks (CNN) have been employed to solve the automation problem [4], [18]- [21] and shown to be superior to conventional denoising or segmentation methods, even with limited training data. However, as we show below, conventional CNNs are a significant bottleneck in the overall workflow due to the several hundred convolution operations involved.…”