Tomography Data Acquisition Research Articles

Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach

The prediction of material properties from a given microstructure and its reverse engineering displays an essential ingredient for accelerated material design. However, a comprehensive methodology to uncover the processing-structure-property relationship is still lacking. Herein, we develop a methodology capable of understanding this relationship for differently processed porous materials. We utilize a multi-method machine learning approach incorporating tomographic image data acquisition, segmentation, microstructure feature extraction, feature importance analysis and synthetic microstructure reconstruction. Enhanced segmentation with an accuracy of about 95% based on an efficient annotation technique provides the basis for accurate microstructure quantification, prediction and understanding of the correlation of the extracted microstructure features and electrical conductivity. We show that a diffusion probabilistic model superior to a generative adversarial network model, provides synthetic microstructure images including physical information in agreement with real data, an essential step to predicting properties of unseen conditions.

Comparison of Different Quantum Computing Devices for Optimization of Computed Tomography Data Acquisition

Comparison of Different Quantum Computing Devices for Optimization of Computed Tomography Data Acquisition

Optimization of Computed Tomography Data Acquisition by Means of Quantum Computing

Quantum Computing (QC) technology has made tremendous progress recently. Today, first real QC systems are operational. The Fraunhofer Institute of Integrated Circuits IIS, Division Development Center X-ray Technology is participating in the project BayQS, which aims at identifying and evaluating potential applications of quantum computing in the field of non-destructive testing by means of X-ray imaging. Computed tomography (CT) is a well-established method of 3-D imaging in medicine and industry since several decades. The quality of the resulting volumetric datasets depends essentially on a set of around 20 physical parameters controlling the set-up of X-ray source, X-ray digital detector array, movable mechanical components, sample rotation, and image processing. Hence, we started investigating the potential use of a quantum-based optimization method to optimize CT data acquisition. As a first approach, we focused on the task to minimize the number of projection images required to achieve unaltered image quality when compared to an acquisition over the full 4π solid angle. Minimizing involves a cost function. There are several ways to define this cost function. In order to transfer the task to a QC system, the problem is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO). A decisive feature of the QUBO is, that is can be solved by a hybrid implementation on quantum devices, which combines a classical part to iteratively find the maximum of the cost function and a quantum circuit to evaluate the respective cost function for each combination of projections. This procedure is called quantum approximate optimization algorithm (QAOA). The experiments were performed via the qiskit script language on an IBM Quantum System One located in Ehingen, Germany. This QC system offers 27 qubits and enables first real world experiments with small test datasets.

Real-time imaging of traumatic brain injury using magnetic induction tomography

Objective. Early diagnosis of traumatic brain injury (TBI) is crucial for its prognosis; however, traditional computed tomography diagnostic methods rely on large medical devices with an associated lag time to receive results. Therefore, an imaging modality is needed that provides real-time monitoring, can easily be carried out to assess the extent of TBI damage, and thus guides treatment. Approach. In the present study, an improved magnetic induction tomography (MIT) data acquisition system was used to monitor TBI in an animal model and distinguish the injury level. A pneumatically controlled cortical impactor was used to strike the parietal lobe of anesthetized rabbits two or three times under the same parameter mode to establish two different rabbit models of TBI. The MIT data acquisition system was used to record data and continuously monitor the brain for one hour without intervention. Main results. A target with increased conductivity was clearly observed in the reconstructed image. The position was relatively fixed and accurate, and the average positioning error of the image was 0.013 72 m. The normalized mean reconstruction value of all images increased with time. The slope of the regression line of the normalized mean reconstruction value differed significantly between the two models (p < 0.0001). Significance. This indicates that in the animal model, the unique features of MIT may facilitate the early monitoring of TBI and distinguish different degrees of injuries, thereby reducing the risk and mortality of associated complications.

Overcoming the data processing challenges of unifying tomography techniques at ESRF

The ESRF tomography software is undergoing a major re-write, targeted at unifying the tomography user experience, data acquisition, data format, and processing tools. To cope with the inherent large complexity of handling data coming from multiple beamlines, techniques and facilities, we are developing an open source software called NXtomomill. It is named after the NXtomo application class of the NeXus data format, and it will offer advanced data conversion, manipulation and reduction functions. This includes azimuthal integration for X-ray Diffraction CT, and elemental fitting for X-ray Fluorescence CT.This work will have a remarkable impact on resource management, software robustness, and data storage. With the addition of the new functionality of the ESRF data portal, it will also unlock unimagined opportunities with respect to the automation of artificial intelligence techniques on large and heterogeneous collections of datasets.

Fast cryo-electron tomography data acquisition to determine molecular structures in situ.

Fast cryo-electron tomography data acquisition to determine molecular structures in situ.

Hyperspectral three-dimensional absorption imaging using snapshot optical tomography.

Hyperspectral imaging (HSI) records a series of two-dimensional (2D) images for different wavelengths to provide the chemical fingerprint at each pixel. Combining HSI with a tomographic data acquisition method, we can obtain the chemical fingerprint of a sample at each point in three-dimensional (3D) space. The so-called 3D HSI typically suffers from low imaging throughput due to the requirement of scanning the wavelength and rotating the beam or sample. In this paper we present an optical system which captures the entire four-dimensional (4D), i.e., 3D structure and 1D spectrum, dataset of a sample with the same throughput of conventional HSI systems. Our system works by combining snapshot projection optical tomography (SPOT) which collects multiple projection images with a single snapshot, and Fourier-transform spectroscopy (FTS) which results in superior spectral resolution by collecting and processing a series of interferogram images. Using this hyperspectral SPOT system we imaged the volumetric absorbance of dyed polystyrene microbeads, oxygenated red blood cells (RBCs), and deoxygenated RBCs. The 4D optical system demonstrated in this paper provides a tool for high-throughput chemical imaging of complex microscopic specimens.

A Rapid, Low-Cost, and High-Precision Multifrequency Electrical Impedance Tomography Data Acquisition System for Plant Phenotyping

Plant phenotyping plays an important role for the thorough assessment of plant traits such as growth, development, and physiological processes with the target of achieving higher crop yields by the proper crop management. The assessment can be done by utilizing two- and three-dimensional image reconstructions of the inhomogeneities. The quality of the reconstructed image is required to maintain a high accuracy and a good resolution, and it is desirable to reconstruct the images with the lowest possible noise. In this work, an electrical impedance tomography (EIT) data acquisition system is developed for the reconstruction and evaluation of the inhomogeneities by utilizing a non-destructive method. A high-precision EIT system is developed by designing an electrode array sensor using a cylindrical domain for the measurements in different planes. Different edible plant slices along with multiple plant roots are taken in the EIT domain to assess and calibrate the system, and their reconstructed results are evaluated by utilizing an impedance imaging technique. A non-invasive imaging is carried out in multiple frequencies by utilizing a difference method of reconstruction. The performance and accuracy of the EIT system are evaluated by measuring impedances between 1 and 100 kHz using a low-cost and rapid electrical impedance spectroscopy (EIS) tool connected to the sensor. A finite element method (FEM) modeling is utilized for image reconstruction, which is carried out using electrical impedance and diffuse optical tomography reconstruction software (EIDORS). The reconstruction is made successfully with the optimized results obtained using Gauss–Newton (GN) algorithms.

High-resolution structure determination using high-throughput electron cryo-tomography.

Tomographic reconstruction of frozen-hydrated specimens followed by extraction and averaging of sub-tomograms has successfully been used to determine the structure of macromolecules in their native environment at resolutions that are high enough to reveal molecular level interactions. The low throughput characteristic of tomographic data acquisition combined with the complex data-analysis pipeline that is required to obtain high-resolution maps, however, has limited the applicability of this technique to favorable samples or to resolutions that are too low to provide useful mechanistic information. Recently, beam image-shift electron cryo-tomography (BISECT), a strategy to significantly accelerate the acquisition of tilt series without sacrificing image quality, was introduced. The ability to produce thousands of high-quality tilt series during a single microscope session, however, introduces significant bottlenecks in the downstream data analysis, which has so far relied on specialized pipelines. Here, recent advances in accurate estimation of the contrast transfer function and self-tuning exposure-weighting routines that contribute to improving the resolution and streamlining the structure-determination process using sub-volume averaging are reviewed. Ultimately, the combination of automated data-driven techniques for image analysis together with high-throughput strategies for tilt-series acquisition will pave the way for tomography to become the technique of choice for in situ structure determination.

Integration of imaging modalities in digital dental workflows - possibilities, limitations, and potential future developments.

The digital workflow process follows different steps for all dental specialties. However, the main ingredient for the diagnosis, treatment planning and follow-up workflow recipes is the imaging chain. The steps in the imaging chain usually include all or at least some of the following modalities: cone-beam computed tomographic data acquisition, segmentation of the cone-beam computed tomography image, intraoral scanning, facial three-dimensional soft tissue capture and superimposition of all the images for the creation of a virtual augmented model. As a relevant clinical problem, the accumulation of error at each step of the chain might negatively influence the final outcome. For an efficient digital workflow, it is important to be aware of the existing challenges within the imaging chain. Furthermore, artificial intelligence-based strategies need to be integrated in the future to make the workflow more simplified, accurate and efficient.

Automated High-Speed S-Wave Velocity Tomographic System for Laboratory Geotechnical Engineering Process Monitoring – A Study on Pile Installation

Abstract This paper reports the innovative development of a flexible, multiple-layer, economical but robust, and automated high-speed shear wave (Vs) tomographic control and data acquisition system for process monitoring in the laboratory and the associated validation for monitoring the dynamic process of model pile installation. This tailor-made tomographic system consists of four major parts: (i) the control unit built with a microcontroller board, (ii) a signal generating unit, (iii) three layers of bender element sensing arrays (including 14 source and receiver bender elements per layer), and (iv) a signal receiving unit. The system allows for sampling rates up to 1 GS/s (20 MS/s were used in this study); the required total measurement time is 2.94 seconds (i.e., for 196 ray path measurements) to allow for dynamic process monitoring. The obtained time-lapse Vs tomographic images during pile installation show that Vs first gradually increases when the pile toe is approaching the sensing layer and gradually decreases afterward as the pile toe penetrates through the layer. The validity of the Vs tomographic images is supported by similar responses in variations of the circumferential stress σc′ and radial stress σr′, measured by the tactile pressure sensors.

Effect of Saturation and Image Resolution on Representative Elementary Volume and Topological Quantification: An Experimental Study on Bentheimer Sandstone Using Micro-CT

The establishment of the representative elementary volume (REV) in studies of porous media is crucial for linking microscopic structure and pore-scale fluid configurations to macroscopic flow processes. We present analysis of the REV of porosity, specific interfacial area, and topological measures (Betti numbers and the Euler characteristic) for an air–water–Bentheimer sandstone system imaged using micro-CT at multiple wetting-phase saturation levels, acquired during primary imbibition (an unsteady-state displacement process). The original high-resolution tomographic data (1.66 μm voxel size) were downsampled to provide additional images at a range of voxel sizes, and REV analysis was also performed for these images. The results demonstrate that the REV is dependent on the image resolution: at large voxel size features are lost, resulting in unreliable parameter measurements. The wetting-phase saturation impacts the REV of the specific interfacial area, depending on the phase pair under consideration. Quantification of topological measures is especially sensitive to voxel size, as microporosity associated with clays in this sandstone sample significantly biases the results; treatment of microporosity is required to obtain connectivity results applicable to flow processes occurring within macropores. Additionally, the REV of the Euler characteristic is dependent on the heterogeneous structure of the air phase at intermediate saturations during the unsteady-state displacement processes. These results inform experimental design and 3D tomographic data acquisition parameter targets and provide insight into the microstructure of fluid phases during primary imbibition.

Image reconstruction in electrical impedance tomography using a reconfigurable FPGA system

A networked tomographic data acquisition device architectured with FPGA is developed to provide remotely-reconfigurable waveform functions for emission of excitation signals. The purpose of the device is to explore online data acquisition techniques for medical applications. Dedicated electrodes for excitation and sensing were selected for the electrical perturbation of the media under study. This device precisely controls input and acquire output in synchrony. The envisaged use is particularly the exploitation of signal modulation to achieve selective response levels in live tissue. In this work we study the acquired data, simulate the measuring scheme selected for electrical perturbation using the finite element method, and estimate the impedance image of the media.

Parasitic capacitances estimation of an Electrical Impedance Tomography data acquisition system by Bayesian inference

Electrical Impedance Tomography (EIT) is a technique that produces an image from a current injection inside the vessel. Since the problem is severely ill-posed, errors and noise can lead to instabilities in the image. One of these errors’ primary sources is the parasitic capacitances that shunt the signal to the ground. These non-idealities are present in integrated circuits, cables, printed circuit boards, and others. Thus, it is crucial to determine the quantity of these parasitic capacitances present in the circuit to design methods to avoid these problems. Given these reasons, this work aims to estimate the output impedance and parasitic capacitances from the data acquisition system. For that, the Maximum a Posteriori (MAP) Estimate, and the Monte Carlo Markov Chain (MCMC) is used. The method is validated and utilized to estimate the unknown quantities’ input impedance from a developed system.

Particle-Tracking Proton Computed Tomography-Data Acquisition, Preprocessing, and Preconditioning.

Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.

Методы анализа данных объёмной компьютерной томографии турбинных лопаток

The use of computed tomography slices covering the entire volume of a component has been critical to the advancement of innovative aerospace technologies. Inspection times for these components were often measured in hours, as data were acquired one single computed tomography slice at a time. &#x0D; Recent advancements in computed tomography data acquisition technology have moved from the use of linear X-ray detectors to area X-ray detectors that can collect computed tomography data from a component in a single acquisition. The use of area X-ray detectors to collect computed tomography data is often referred to as volumetric computed tomography (VCT) and has reduced data acquisition times for an entire component from hours to minutes. The reduced inspection time provided by VCT has made it practicable to use this technique for the production inspection of advanced components. In order to realize the use of VCT in a production environment, the data produced by the inspection must be analyzed within a time comparable to the time taken by the data acquisition. This paper describes a technique for rapidly analyzing VCT inspection data and the application of that technique to the inspection of turbine airfoils.

The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction.

An important challenge for deep learning models is generalizing to new datasets that may be acquired with acquisition protocols different from the training set. It is not always feasible to expand training data to the range encountered in clinical practice. We introduce a new technique, physics-based data augmentation (PBDA), that can emulate new computed tomography (CT) data acquisition protocols. We demonstrate two forms of PBDA, emulating increases in slice thickness and reductions of dose, on the specific problem of false-positive reduction in the automatic detection of lung nodules. We worked with CT images from the lung image database consortium (LIDC) collection. We employed a hybrid ensemble convolutional neural network (CNN), which consists of multiple CNN modules (VGG, DenseNet, ResNet), for a classification task of determining whether an image patch was a suspicious nodule or a false positive. To emulate a reduction in tube current, we injected noise by simulating forward projection, noise addition, and backprojection corresponding to 1.5mAs (a "chest x-ray" dose). To simulate thick slice CT scans from thin slice CT scans, we grouped and averaged spatially contiguous CT within thin slice data. The neural network was trained with 10% of the LIDC dataset that was selected to have either the highest tube current or the thinnest slices. The network was tested on the remaining data. We compared PBDA to a baseline with standard geometric augmentations (such as shifts and rotations) and Gaussian noise addition. PBDA improved the performance of the networks when generalizing to the test dataset in a limited number of cases. We found that the best performance was obtained by applying augmentation at very low doses (1.5mAs), about an order of magnitude less than most screening protocols. In the baseline augmentation, a comparable level of Gaussian noise was injected. For dose reduction PBDA, the average sensitivity of 0.931 for the hybrid ensemble network was not statistically different from the average sensitivity of 0.935 without PBDA. Similarly for slice thickness PBDA, the average sensitivity of 0.900 when augmenting with doubled simulated slice thicknesses was not statistically different from the average sensitivity of 0.895 without PBDA. While there were cases detailed in this paper in which we observed improvements, the overall picture was one that suggests PBDA may not be an effective data enrichment tool. PBDA is a newly proposed strategy for mitigating the performance loss of neural networks related to the variation of acquisition protocol between the training dataset and the data that is encountered in deployment or testing. We found that PBDA does not provide robust improvements with the four neural networks (three modules and the ensemble) tested and for the specific task of false-positive reduction in nodule detection.

Improved applicability and robustness of fast cryo-electron tomography data acquisition

The power of cryo-electron tomography (cryoET) lies in its capability to characterize macromolecules in their cellular context. Structure determination by cryoET, however, is time-consuming compared to single particle approaches. A recent study reported significant acceleration of data acquisition by a fast-incremental single-exposure (FISE) tilt series scheme.Here we improved the method and evaluated its efficiency and performance. We show that (1) FISE combined with the latest generation of direct electron detectors speeds up collection considerably, (2) previous generation (pre-2017) double-tilt axis Titan Krios holders are also suitable for FISE data acquisition, (3) x, y and z-specimen shifts can be compensated for, and (4) FISE tilt series data can generate averages of sub-nanometer resolution.These advances will allow for a widespread adoption of cryoET for high-throughput in situ studies and high-resolution structure determination across different biological research disciplines.

A Description of Field Setup and Common Issues in 2-D Electrical Resistivity Tomography Data Acquisition

Electrical resistivity tomography is a geophysical imaging technique utilized commonly for shallow subsurface investigations in support of civil structural, environmental, utility and archaeological investigations. For engineering consulting businesses that use this survey method extensively, the acquisition of reliable resistivity data using proper equipment in a timely manner is crucial for data processing, interpretation, and overall client satisfaction. Tough site conditions and human errors can negatively affect the speed of acquisition, data quality and hence affect the overall project timeline and cost. Based on the extensive use of an AGISuperSting resistivity meter system, this paper addresses the general surveying procedures with emphasis on common problems encountered and offers solutions accordingly. The most common issues are improper connections, hard or dry ground, overgrown vegetation and rain at the surveying site.Operations that could speed up the process can also be beneficial for engineers and researchers who acquires the data in a similar setting.

A Methylcellulose Hydrogel as Support for 3D Plotting of Complex Shaped Calcium Phosphate Scaffolds.

3D plotting is an additive manufacturing technology enabling biofabrication, thus the integration of cells or biologically sensitive proteins or growth factors into the manufacturing process. However, most (bio-)inks developed for 3D plotting were not shown to be processed into clinically relevant geometries comprising critical overhangs and cavities, which would collapse without a sufficient support material. Herein, we have developed a support hydrogel ink based on methylcellulose (mc), which is able to act as support as long as the co-plotted main structure is not stable. Therefore, 6 w/v %, 8 w/v % and 10 w/v % mc were allowed to swell in water, resulting in viscous inks, which were characterized for their rheological and extrusion properties. The successful usage of 10 w/v % mc as support ink was proven by multichannel plotting of the support together with a plottable calcium phosphate cement (CPC) acting as main structure. CPC scaffolds displaying critical overhangs or a large central cavity could be plotted accurately with the newly developed mc support ink. The dissolution properties of mc allowed complete removal of the gel without residuals, once CPC setting was finished. Finally, we fabricated a scaphoid bone model by computed tomography data acquisition and co-extrusion of CPC and the mc support hydrogel.