Enhanced quantification for 3D SEM-EDS: using the full set of available X-ray lines

Lowering latency and processing burden in computational imaging through dimensionality reduction of the sensing matrix

Recent demonstrations have shown that frequency-diverse computational imaging systems can greatly simplify conventional architectures developed for imaging by transferring constraints into the digital layer. Here, in order to limit the latency and processing burden involved in image reconstruction, we propose to truncate insignificant principal components of the sensing matrix that links the measurements to the scene to be imaged. In contrast to recent work using principle component analysis to synthesize scene illuminations, our generic approach is fully unsupervised and is applied directly to the sensing matrix. We impose no restrictions on the type of imageable scene, no training data is required, and no actively reconfigurable radiating apertures are employed. This paper paves the way to the constitution of a new degree of freedom in image reconstructions, allowing one to place the performance emphasis either on image quality or latency and computational burden. The application of such relaxations will be essential for widespread deployment of computational microwave and millimeter wave imagers in scenarios such as security screening. We show in this specific context that it is possible to reduce both the processing time and memory consumption with a minor impact on the quality of the reconstructed images.

Inverse modeling for quantitative X-ray microanalysis applied to 2D heterogeneous materials

Current quantitative X-ray microanalysis methods are only available for homogeneous materials. This paper presents a newly developed inverse modeling algorithm to determine both the structure and composition of two-dimensional (2D) heterogeneous materials from a series of X-ray intensity measurements under different beam energies and beam positions. It utilizes an iterative process of forward modeling to determine the optimal specimen to minimize the relative differences between the simulated and experimental characteristic X-ray intensities. The Monte Carlo method is used for the forward modeling to predict the X-ray radiation for a given specimen and experimental setup. Several examples of applications are presented for different types of samples with one-dimensional (1D) and 2D structures, in which the simulated X-ray intensities from phantom samples are used as input. Most of the results obtained from our algorithm agree well with the phantom samples. Some discrepancies are found for the voxels located at deeper depths of the 2D samples. And the discrepancies may be attributed to errors from the Monte Carlo simulations and from the variation of the X-ray range with beam energy. As a proof-of-concept work, this paper confirms the feasibility of our inverse modeling algorithm applied to 2D heterogeneous materials.