This study explores the feasibility of using structured illumination with coherent light in dark-field X-ray microscopy (DFXM) through simplified numerical simulations. By introducing phase and amplitude modulation via a slit aperture, we investigate the potential to enhance spatial resolution and photon utilization without sacrificing beam flux. A gradient descent-based solver is employed to analyze the reconstruction quality of 3D samples, demonstrating the method’s ability to recover fine structural details under these simplified conditions. Although the findings are preliminary, they suggest a promising direction for integrating coherent encoding strategies into DFXM, potentially enabling more efficient imaging of embedded microstructures and strain fields in crystalline materials. This work lays the foundation for future experimental validation and the development of advanced reconstruction algorithms.

This work extends our previous research entitled “MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization” that was originally published at SC19 conference (Hidayetoğluet al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11K×11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.

X-ray computed tomography is a commonly used technique for noninvasive imaging at synchrotron facilities. Iterative tomographic reconstruction algorithms are often preferred for recovering high quality 3D volumetric images from 2D X-ray images, however, their use has been limited to small/medium datasets due to their computational requirements. In this paper, we propose a high-performance iterative reconstruction system for terabyte(s)-scale 3D volumes. Our design involves three novel optimizations: (1) optimization of (back)projection operators by extending the 2D memory-centric approach to 3D;(2) performing hierarchical communications by exploiting “fat-node” architecture with many GPUs; 3) utilization of mixed-precision types while preserving convergence rate and quality. We extensively evaluate the proposed optimizations and scaling on the Summit supercomputer. Our largest reconstruction is a mouse brain volume with 9×11K×11K voxels, where the total reconstruction time is under three minutes using 24,576 GPUs, reaching 65 PFLOPS: 34% of Summit's peak performance.

In this paper, we present a novel approach based on deep neural network for solving the limited angle tomography problem. The limited angle views in tomography cause severe artifacts in the tomographic reconstruction. We use deep convolutional generative adversarial networks (DCGAN) to fill in the missing information in the sino-gram domain. By using the continuity loss and the two-ends method, the image completion in the sinogram domain is done effectively, resulting in high quality reconstructions with fewer artifacts. The sinogram completion method can be applied to different problems such as ring artifact removal and truncated tomography problems.

X-ray computed tomography (XCT)is used regularly at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be terabyte-scale. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use exception rather than the rule. We propose here a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures incolving KNL and GPU. MemXCT can reconstruct a large (11K×11K) mouse brain tomogram in ~10 seconds using 4096 KNL nodes (256K cores), the largest iterative reconstruction achieved in near-real time.

Modern scientific instruments, such as detectors at synchrotron light sources, can generate data at 10s of GB/sec. Current experimental protocols typically process and validate data only after an experiment has completed, which can lead to undetected errors and prevents online steering. Real-time data analysis can enable both detection of, and recovery from, errors, and optimization of data acquisition. We thus propose an autonomous stream processing system that allows data streamed from beamline computers to be processed in real time on a remote supercomputer, with a control feed-back loop used to make decisions during experimentation. We evaluate our system using two iterative tomographic reconstruction algorithms and varying data generation rates. These experiments are performed in a real-world environment in which data are streamed from a light source to a cluster for analysis and experimental control. We demonstrate that our system can sustain analysis rates of hundreds of projections per second by using up to 1,200 cores, while meeting stringent data quality constraints.