Doga Gursoy

Nanoscale 3D imaging at the Advanced Photon Source

Over the past decade, technology breakthroughs in the field of x-ray optics have enabled the development of advanced imaging nanoprobes at third-generation synchrotrons.1–11 X-rays have unique capabilities in terms of resolution, sensitivity, and speed, and by combining these properties with their ability to penetrate matter, these new instruments have played an important role in the recent advent of nano-material-related research.12 The gap— in terms of spatial resolution—between such x-ray instruments and electron microscopes, however, still needs to be reduced. In addition, it remains a challenge to offer in situ measurement capabilities while simultaneously pushing the spatial resolution limits. Conceptually, transmission x-ray microscopes (TXMs) are similar to optical visible light microscopes. In these instruments, tunable monochromatic x-rays illuminate the condenser—either an ellipsoidal glass mono-capillary or special type of diffraction grating known as a beam-shaping condenser (BSC)—and a Fresnel zone plate (FZP) is used as the objective lens to magnify the images or radiographs (see Figure 1). TXMs are also full-field imaging instruments, meaning that the snapshot images of absorption contrasts inside samples are acquired with 2D detectors (commonly four megapixel sensors). It is this type of full-field imaging—much faster than raster scan modes of pencil beam nanoprobes—which makes dynamic studies possible. To take on the challenge of nano-materials science in the fields of energy storage, microelectronics, nano-porous material functions, as well as life, Earth, and environmental sciences, we have developed a new in-house TXM at the Advanced Photon Source (sector 32-ID) of the Argonne National Laboratory. This instrument has replaced an older, first-generation commercial system,13 by providing a superior analytical imaging performance and in situ capabilities. In addition, our TXM supports a Figure 1. (a) Schematic representation of a transmission x-ray microscope (TXM) used for nano-tomography studies. (b) Photograph of the TXM that has been developed at sector 32-ID of the Argonne National Laboratory’s Advanced Photon Source. -CT: Micro-computed tomography.

Rapid Tomographic Image Reconstruction via Large-Scale Parallelization

Synchrotron (x-ray) light sources permit investigation of the structure of matter at extremely small length and time scales. Advances in detector technologies enable increasingly complex experiments and more rapid data acquisition. However, analysis of the resulting data then becomes a bottleneck—preventing near-real-time error detection or experiment steering. We present here methods that leverage highly parallel computers to improve the performance of iterative tomographic image reconstruction applications. We apply these methods to the conventional per-slice parallelization approach and use them to implement a novel in-slice approach that can use many more processors. To address programmability, we implement the introduced methods in high-performance MapReduce-like computing middleware, which is further optimized for reconstruction operations. Experiments with four reconstruction algorithms and two large datasets show that our methods can scale up to 8 K cores on an IBM BG/Q supercomputer with almost perfect speedup and can reduce total reconstruction times for large datasets by more than 95.4 % on 32 K cores relative to 1 K cores. Moreover, the average reconstruction times are improved from \(\sim \)2 h (256 cores) to \(\sim \)1 min (32 K cores), thus enabling near-real-time use.

Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography

Visual Abstract Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts.

Low-dose x-ray tomography through a deep convolutional neural network

Synchrotron-based X-ray tomography offers the potential for rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short-exposure-time projections enhanced with CNNs show signal-to-noise ratios similar to long-exposure-time projections. They also show lower noise and more structural information than low-dose short-exposure acquisitions post-processed by other techniques. We evaluated this approach using simulated samples and further validated it with experimental data from radiation sensitive mouse brains acquired in a tomographic setting with transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in low-dose datasets enhanced with CNN. This method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens

Rapid alignment of nanotomography data using joint iterative reconstruction and reprojection

As x-ray and electron tomography is pushed further into the nanoscale, the limitations of rotation stages become more apparent, leading to challenges in the alignment of the acquired projection images. Here we present an approach for rapid post-acquisition alignment of these projections to obtain high quality three-dimensional images. Our approach is based on a joint estimation of alignment errors, and the object, using an iterative refinement procedure. With simulated data where we know the alignment error of each projection image, our approach shows a residual alignment error that is a factor of a thousand smaller, and it reaches the same error level in the reconstructed image in less than half the number of iterations. We then show its application to experimental data in x-ray and electron nanotomography.

Direct coupling of tomography and ptychography

A generalization of the ptychographic phase problem is presented for recovering refractive properties of a three-dimensional object in a tomography setting. This approach, which ignores the lateral overlapping probe requirements in existing ptychography algorithms, can enable the reconstruction of objects using highly flexible acquisition patterns and pave the way for sparse and rapid data collection with lower radiation exposure.A generalization of the ptychographic phase problem is presented for recovering refractive properties of a three-dimensional object in a tomography setting. This approach, which ignores the lateral overlapping probe requirements in existing ptychography algorithms, can enable the reconstruction of objects using highly flexible acquisition patterns and pave the way for sparse and rapid data collection with lower radiation exposure.

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

AbstractBackgroundModern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis.MethodsWe present Trace, a data-intensive computing engine that we have developed to enable high-performance implementation of iterative tomographic reconstruction algorithms for parallel computers. Trace provides fine-grained reconstruction of tomography datasets using both (thread-level) shared memory and (process-level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations that we apply to the replicated reconstruction objects and evaluate them using tomography datasets collected at the Advanced Photon Source. ResultsOur experimental evaluations show that our optimizations and parallelization techniques can provide 158× speedup using 32 compute nodes (384 cores) over a single-core configuration and decrease the end-to-end processing time of a large sinogram (with 4501 × 1 × 22,400 dimensions) from 12.5 h to <5 min per iteration.ConclusionThe proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.

Real-Time Data Analysis and Autonomous Steering of Synchrotron Light Source Experiments

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.

A new transmission x-ray microscope for in-situ nano-tomography at the APS(Conference Presentation)

A new Transmission X-ray Microscope (TXM), optimized for in-situ nano-tomography experiments, has been designed and built at the Advanced Photon Source (APS). The instrument has been in operation for the last two years and is supporting users over large fields of Science, from energy storage and material science to natural sciences. The flexibility of our X-ray microscope design permits evolutionary geometries and can accommodate relatively heavy, up to 5 kg, and bulky in-situ cells while ensuring high spatial resolution, which is expected to improve steadily thanks to the support of the RD program led by the APS-Upgrade project on Fresnel zone plates (FZP). The robust sample stack, designed with minimum degrees of freedom shows a stability better than 4 nm rms at the sample location. The TXM operates with optics fabricated in-house. A spatial resolution of 30 nm per voxel has been demonstrated when the microscope operates with a 60 nm outermost zone width FZP with a measured efficiency of 18% at 8 keV. 20 nm FZP are also currently available and should be in routine use within the next few months once a new matching condenser is produced. In parallel, efficiency is being improved with opto-mechanical engineering (FZP stacking system) and software developments (more efficient reconstruction algorithms combined with different data acquisition schemes), enabling 3D dynamic studies when sample evolution occurs within a couple of tens of seconds.

TomoPy: A framework for the analysis of synchrotron tomographic data

Analysis of large tomographic datasets at synchrotron light sources is becoming progressively more challenging due to the increasing data acquisition rates that new technologies in X-ray sources and detectors enable. The next generation of synchrotron facilities that are currently under design or construction throughout the world will provide diffraction limited X-ray sources and is expected to boost the current data rates by several orders of magnitude and stressing the need for the development and integration of efficient analysis tools more than ever. Here we describe in detail an attempt to provide such a collaborative framework for the analysis of synchrotron tomographic data that has the potential to unify the effort of different facilities and beamlines performing similar tasks. The proposed Python/C++ based framework is open-source, OS and data format independent, parallelizable and supports functional programming that many researchers prefer. This collaborative platform will affect all major synchrotron facilities where new effort is now dedicated into developing new tools that can be deployed at the facility for real time processing as well as distributed to users for off site data processing.