Real-time streaming tomographic reconstruction with on-demand data capturing and 3D zooming to regions of interest

TomoPyUI: a user-friendly tool for rapid tomography alignment and reconstruction

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data (i.e. organizing data/metadata, applying image filters etc.) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI, a user interface for the well known tomography data processing package TomoPy. This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI, along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI, and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.

Disturbed flow in the juxta-anastomotic area of an arteriovenous fistula correlates with endothelial loss, acute thrombus formation, and neointimal hyperplasia

Clinical failure of arteriovenous neointimal hyperplasia (NIH) fistulae (AVF) is frequently due to juxta-anastomotic NIH (JANIH). Although the mouse AVF model recapitulates human AVF maturation, previous studies focused on the outflow vein distal to the anastomosis. We hypothesized that the juxta-anastomotic area (JAA) has increased NIH compared with the outflow vein. AVF was created in C57BL/6 mice without or with chronic kidney disease (CKD). Temporal and spatial changes of the JAA were examined using histology and immunofluorescence. Computational techniques were used to model the AVF. RNA-seq and bioinformatic analyses were performed to compare the JAA with the outflow vein. The jugular vein to carotid artery AVF model was created in Wistar rats. The neointima in the JAA shows increased volume compared with the outflow vein. Computational modeling shows an increased volume of disturbed flow at the JAA compared with the outflow vein. Endothelial cells are immediately lost from the wall contralateral to the fistula exit, followed by thrombus formation and JANIH. Gene Ontology (GO) enrichment analysis of the 1,862 differentially expressed genes (DEG) between the JANIH and the outflow vein identified 525 overexpressed genes. The rat jugular vein to carotid artery AVF showed changes similar to the mouse AVF. Disturbed flow through the JAA correlates with rapid endothelial cell loss, thrombus formation, and JANIH; late endothelialization of the JAA channel correlates with late AVF patency. Early thrombus formation in the JAA may influence the later development of JANIH.NEW & NOTEWORTHY Disturbed flow and focal endothelial cell loss in the juxta-anastomotic area of the mouse AVF colocalizes with acute thrombus formation followed by late neointimal hyperplasia. Differential flow patterns between the juxta-anastomotic area and the outflow vein correlate with differential expression of genes regulating coagulation, proliferation, collagen metabolism, and the immune response. The rat jugular vein to carotid artery AVF model shows changes similar to the mouse AVF model.

Alrecon: computed tomography reconstruction web application based on Solara

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

Micrometer-scale structure in shark vertebral centra

The vertebral centra of sharks consist of cartilage, and many species' centra contain a bioapatite related to that in bone. Centra microarchitectures at the 0.5-50 µm scale do not appear to have been described previously. This study examines centrum microarchitecture in lamniform and carcharhiniform sharks with synchrotron microComputed Tomography (microCT), scanning electron microscopy and spectroscopy and light microscopy. The analysis centers on the blue shark (carcharhiniform) and shortfin mako (lamniform), species studied with all three modalities. Synchrotron microCT results from seven other species complete the report. The main centrum structures, the corpus calcareum and intermedialia, consist of fine, closely-spaced, mineralized trabeculae whose mean thicknesses and spacings range from 4.5 to 11.2 µm and 4.5 to 15.6 µm, respectively. A significant (p = 0.00001) positive linear relationship between and exists for multiple positions within one mako centrum. Carcharhiniform species' and exhibit an inverse linear relationship (p = 0.005) while in lamniforms these variables tend toward a positive relationship which does not reach statistical significance (p = 0.099). In all species, the trabeculae form an uninterrupted, interconnected network, and the unmineralized volumes are similarly interconnected. Small differences in mineralization level are observed in trabeculae. Centrum growth band pairs are found to consist of locally higher /lower mineral volume fraction. Within the intermedialia, radial canals and radial microrods were characterized, and compacted trabeculae are prominent in the mako intermedialia. The centra's mineralized central zones were non-trabecular and are also described. STATEMENT OF SIGNIFICANCE: This study's novel result is the demonstration that the mineralized cartilage of sharks' vertebral bodies (centra) consists of a fine 3D array of interconnected plates (trabeculae) and an interpenetrating network of unmineralized tissue. This microstructure is radically different from that in tesserae or in teeth, the other main mineralized shark tissues. Using volumetric synchrotron microComputed Tomography, numerical values of mean trabecular thickness and spacing and their relationship were measured for nine species. Scanning electron microscopy added a higher resolution view of the microstructures, and histology provided complementary information on cartilage and cells. The present results suggest centra microstructure helps accommodate the very large in vivo strains and may prevent damage accumulation during millions of cycles of swimming-induced loading.

tomoCAM: fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms

X-ray-based computed tomography is a well established technique for determining the three-dimensional structure of an object from its two-dimensional projections. In the past few decades, there have been significant advancements in the brightness and detector technology of tomography instruments at synchrotron sources. These advancements have led to the emergence of new observations and discoveries, with improved capabilities such as faster frame rates, larger fields of view, higher resolution and higher dimensionality. These advancements have enabled the material science community to expand the scope of tomographic measurements towards increasingly in situ and in operando measurements. In these new experiments, samples can be rapidly evolving, have complex geometries and restrictions on the field of view, limiting the number of projections that can be collected. In such cases, standard filtered back-projection often results in poor quality reconstructions. Iterative reconstruction algorithms, such as model-based iterative reconstructions (MBIR), have demonstrated considerable success in producing high-quality reconstructions under such restrictions, but typically require high-performance computing resources with hundreds of compute nodes to solve the problem in a reasonable time. Here, tomoCAM, is introduced, a new GPU-accelerated implementation of model-based iterative reconstruction that leverages non-uniform fast Fourier transforms to efficiently compute Radon and back-projection operators and asynchronous memory transfers to maximize the throughput to the GPU memory. The resulting code is significantly faster than traditional MBIR codes and delivers the reconstructive improvement offered by MBIR with affordable computing time and resources. tomoCAM has a Python front-end, allowing access from Jupyter-based frameworks, providing straightforward integration into existing workflows at synchrotron facilities.

Just-in-time deep learning for real-time X-ray computed tomography

Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and analysis components, Deep Neural Networks (DNNs) are a promising technology, due to their strong performance and much faster run-times compared to conventional algorithms. DNNs may prevent experiment repetition by simplifying real-time steering and optimization of the ongoing experiment. The main challenge of integrating DNNs into real-time tomography pipelines, however, is that they need to learn their task from representative data before the start of the experiment. In scientific environments, such training data may not exist, and other uncertain and variable factors, such as the set-up configuration, reconstruction parameters, or user interaction, cannot easily be anticipated beforehand, either. To overcome these problems, we developed just-in-time learning, an online DNN training strategy that takes advantage of the spatio-temporal continuity of consecutive reconstructions in the tomographic pipeline. This allows training and deploying comparatively small DNNs during the experiment. We provide software implementations, and study the feasibility and challenges of the approach by training the self-supervised Noise2Inverse denoising task with X-ray data replayed from real-world dynamic experiments.

A Compact Two-Dimensional Varifocal Scanning Imaging Device Actuated by Artificial Muscle Material

This paper presents a compact two-dimensional varifocal-scanning imaging device, with the capability of continuously variable focal length and a large scanning range, actuated by artificial muscle material. The varifocal function is realized by the principle of laterally shifting cubic phase masks and the scanning function is achieved by the principle of the decentered lens. One remarkable feature of these two principles is that both are based on the lateral displacements perpendicular to the optical axis. Artificial muscle material is emerging as a good choice of soft actuators capable of high strain, high efficiency, fast response speed, and light weight. Inspired by the artificial muscle, the dielectric elastomer is used as an actuator and produces the lateral displacements of the Alvarez lenses and the decentered lenses. A two-dimensional varifocal scanning imaging device prototype was established and validated through experiments to verify the feasibility of the proposed varifocal-scanning device. The results showed that the focal length variation of the proposed varifocal scanning device is up to 4.65 times higher (31.6 mm/6.8 mm), and the maximum scanning angle was 26.4°. The rise and fall times were 110 ms and 185 ms, respectively. Such a varifocal scanning device studied here has the potential to be used in consumer electronics, endoscopy, and microscopy in the future.

Hierarchically guided in situ nanolaminography for the visualisation of damage nucleation in alloy sheets

Hierarchical guidance is developed for three-dimensional (3D) nanoscale X-ray imaging, enabling identification, refinement, and tracking of regions of interest (ROIs) within specimens considerably exceeding the field of view. This opens up new possibilities for in situ investigations. Experimentally, the approach takes advantage of rapid multiscale measurements based on magnified projection microscopy featuring continuous zoom capabilities. Immediate and continuous feedback on the subsequent experimental progress is enabled by suitable on-the-fly data processing. For this, by theoretical justification and experimental validation, so-called quasi-particle phase-retrieval is generalised to conical-beam conditions, being key for sufficiently fast computation without significant loss of imaging quality and resolution compared to common approaches for holographic microscopy. Exploiting 3D laminography, particularly suited for imaging of ROIs in laterally extended plate-like samples, the potential of hierarchical guidance is demonstrated by the in situ investigation of damage nucleation inside alloy sheets under engineering-relevant boundary conditions, providing novel insight into the nanoscale morphological development of void and particle clusters under mechanical load. Combined with digital volume correlation, we study deformation kinematics with unprecedented spatial resolution. Correlation of mesoscale (i.e. strain fields) and nanoscale (i.e. particle cracking) evolution opens new routes for the understanding of damage nucleation within sheet materials with application-relevant dimensions.

TomocuPy - efficient GPU-based tomographic reconstruction with asynchronous data processing

Fast 3D data analysis and steering of a tomographic experiment by changing environmental conditions or acquisition parameters require fast, close to real-time, 3D reconstruction of large data volumes. Here a performance-optimized TomocuPy package is presented as a GPU alternative to the commonly used central processing unit (CPU) based TomoPy package for tomographic reconstruction. TomocuPy utilizes modern hardware capabilities to organize a 3D asynchronous reconstruction involving parallel read/write operations with storage drives, CPU-GPU data transfers, and GPU computations. In the asynchronous reconstruction, all the operations are timely overlapped to almost fully hide all data management time. Since most cameras work with less than 16-bit digital output, the memory usage and processing speed are furthermore optimized by using 16-bit floating-point arithmetic. As a result, 3D reconstruction with TomocuPy became 20-30 times faster than its multi-threaded CPU equivalent. Full reconstruction (including read/write operations and methods initialization) of a 2048³ tomographic volume takes less than 7 s on a single Nvidia Tesla A100 and PCIe 4.0 NVMe SSD, and scales almost linearly increasing the data size. To simplify operation at synchrotron beamlines, TomocuPy provides an easy-to-use command-line interface. Efficacy of the package was demonstrated during a tomographic experiment on gas-hydrate formation in porous samples, where a steering option was implemented as a lens-changing mechanism for zooming to regions of interest.