Real Data Reconstruction Research Articles

EUSO-Offline: A comprehensive simulation and analysis framework

The complexity of modern cosmic ray observatories and the rich data sets they capture often require a sophisticated software framework to support the simulation of physical processes, detector response, as well as reconstruction and analysis of real and simulated data. Here we present the EUSO-Offline framework. The code base was originally developed by the Pierre Auger Collaboration, and portions of it have been adopted by other collaborations to suit their needs. We have extended this software to fulfill the requirements of Ultra-High Energy Cosmic Ray detectors and very high energy neutrino detectors developed for the Joint Exploratory Missions for an Extreme Universe Observatory (JEM-EUSO). These path-finder instruments constitute a program to chart the path to a future space-based mission like POEMMA. For completeness, we describe the overall structure of the framework developed by the Auger collaboration and continue with a description of the JEM-EUSO simulation and reconstruction capabilities. The framework is written predominantly in modern C++ (compliled against C++17) and incorporates third-party libraries chosen based on functionality and our best judgment regarding support and longevity. Modularity is a central notion in the framework design, a requirement for large collaborations in which many individuals contribute to a common code base and often want to compare different approaches to a given problem. For the same reason, the framework is designed to be highly configurable, which allows us to contend with a variety of JEM-EUSO missions and observation scenarios. We also discuss how we incorporate broad, industry-standard testing coverage which is necessary to ensure quality and maintainability of a relatively large code base, and the tools we employ to support a multitude of computing platforms and enable fast, reliable installation of external packages. Finally, we provide a few examples of simulation and reconstruction applications using EUSO-Offline.

Geometry Parameter Estimation for Sparse X-Ray Log Imaging

We consider geometry parameter estimation in industrial sawmill fan-beam X-ray tomography. In such industrial settings, scanners do not always allow identification of the location of the source–detector pair, which creates the issue of unknown geometry. This work considers an approach for geometry estimation based on the calibration object. We parametrise the geometry using a set of 5 parameters. To estimate the geometry parameters, we calculate the maximum cross-correlation between a known-sized calibration object image and its filtered backprojection reconstruction and use differential evolution as an optimiser. The approach allows estimating geometry parameters from full-angle measurements as well as from sparse measurements. We show numerically that different sets of parameters can be used for artefact-free reconstruction. We deploy Bayesian inversion with first-order isotropic Cauchy difference priors for reconstruction of synthetic and real sawmill data with a very low number of measurements.

Synthesized Image Reconstruction for Post-Reconstruction Resolution Recovery.

Resolution recovery (RR) techniques in positron emission tomography (PET) imaging aim to mitigate spatial resolution losses and related inaccuracies in quantification by using a model of the system's point spread function (PSF) during reconstruction or post-processing. However, including PSF modeling in fully 3-D image reconstruction is far from trivial as access to the scanner-specific forward and back-projectors is required, along with access to the 3-D sinogram data. Hence, post-reconstruction RR methods, such as the Richardson-Lucy (RL) algorithm, can be more practical. However, the RL method leads to relatively rapid noise amplification in early image iterations, giving inferior image quality compared to iterates obtained by placing the PSF model in the reconstruction algorithm. We propose a post-reconstruction RR method by synthesizing PET data by a forward projection of an initial real data reconstruction (such reconstructions are usually available via a scanner's standard reconstruction software). The synthetic PET data are then used to reconstruct an image, but crucially now including a modeled PSF within the system model used during reconstruction. Results from simulations and real data demonstrate the proposed method improves image quality compared to the RL algorithm, whilst avoiding the need for scanner-specific projectors and raw sinogram data (as required by standard PSF modeling within reconstruction).

EPRI sparse reconstruction method based on deep learning

Electron paramagnetic resonance imaging (EPRI) is an advanced tumor oxygen concentration imaging method. Now, the bottleneck problem of EPRI is that the scanning time is too long. Sparse reconstruction is an effective and fast imaging method, which means reconstructing images from sparse-view projections. However, the EPRI images sparsely reconstructed by the classic filtered back projection (FBP) algorithm often contain severe streak artifacts, which affect subsequent image processing. In this work, we propose a feature pyramid attention-based, residual, dense, deep convolutional network (FRD-Net) to suppress the streak artifacts in the FBP-reconstructed images. This network combines residual connection, attention mechanism, dense connections and introduces perceptual loss. The EPRI image with streak artifacts is used as the input of the network and the output-label is the corresponding high-quality image densely reconstructed by the FBP algorithm. After training, the FRD-Net gets the capability of suppressing streak artifacts. The real data reconstruction experiments show that the FRD-Net can better improve the sparse reconstruction accuracy, compared with three existing representative deep networks.

Tabu-DART: a dynamic update strategy for efficient discrete algebraic reconstruction

In X-ray computed tomography, discrete tomography (DT) algorithms have been successful at reconstructing objects composed of only a few distinct materials. Many DT-based methods rely on a divide-and-conquer procedure to reconstruct the volume in parts, which improves their run-time and reconstruction quality. However, this procedure is based on static rules, which introduces redundant computation and diminishes the efficiency. In this work, we introduce an update strategy framework that allows for dynamic rules and increases control for divide-and-conquer methods for DT. We illustrate this framework by introducing Tabu-DART, which combines our proposed framework with the Discrete Algebraic Reconstruction Technique (DART). Through simulated and real data reconstruction experiments, we show that our approach yields similar or improved reconstruction quality compared to DART, with substantially lower computational complexity.

Official and Real Biographies in Context of Soviet Tradition of Non-Material Incentives for Labor during Great Patriotic War

The question of the use of the official labor biography in the 30—40s of the twentieth century as a means of instilling a sacred attitude to work in a Soviet person, a method of non-material stimulation of the production process is considered. The relevance of the study is due to the interest in biography in the context of the history of labor, in the authors’ appeal to the problem of forming a new attitude to work during the period of industrialization and the years of the Great Patriotic War. Attention is paid to the role of periodicals. The results of a comparative analysis of the official and real biography, recovered from the materials of the personal file, are presented. The question is raised about the tasks of the official biography of the Hero of Socialist Labor in this period. The novelty of the research is seen in the attraction of unpublished data from the production archive of the Gorky Railway, in the reconstruction of real biographical data. The authors compare the official and real biographies. The possibility of using heroic biography as a method of non-material stimulation of labor has been proved. The author’s reconstruction of the biography of Ivan Georgievich Makarov — Hero of Socialist Labor is presented. The experience of analytical research of documentary historical sources and their comparison with the narrative tradition is described.

New calculation method for exact length weighting factor in cone‐beam computed tomography

AbstractIn iterative reconstruction algorithms, it is very important to calculate the weighting factor quickly and accurately, which directly affects the reconstruction efficiency and accuracy of computed tomography images. In this paper, a new calculation method for accurate length weighting factor is developed. Firstly, the ray equation is determined according to the coordinates of the source and the ray's projection. Second, the intersections of the ray and the equidistant parallel planes along three coordinate axes are calculated, and the points intersecting with the reconstructed object are put into a set. Finally, when the X‐axis coordinates of all points in the set are different, the points are arranged according to their X‐axis coordinates; when the X‐axis coordinates of all points are same, the points are arranged according to their Y‐axis coordinates. The sorted point set is the intersections that the ray traverses the reconstructed object in sequence. The index and weighting factor of all voxels that the ray passes through can be determined by the sorted point set. Compared with the classical Siddon's method, it avoids the step of describing the intersections with parameters, and its calculation speed is increased by nearly 20%. The numerical simulations and real data reconstruction experiments are conducted to validate the proposed calculation method of exact length weighting factor.

Well logs reconstruction by DCT based IPRM and genetic algorithm

Hydrocarbon production from a particular layer requires careful identification of the stratigraphic boundaries and choosing the right perforation against the face of oil-bearing intervals.Excessive perforations may cause unwanted water production from adjacent layers. Nowadays, clustering of well log data through various methods such as self-organized maps (SOM) helps to find hydrocarbon intervals from other layers. SOM is a type of artificial neural network, which uses the non-monitored learning of the multi-dimensional logging observations to create a 2D clustering map. However, the noisy response data greatly reduces accuracy of the methods by increasing correlation of different data groups. Although spectral such as discrete wavelet transform (DWT) can reduce correlation and reconstruct the logs that no longer fluctuate as before, they have two major limitations. First, responses smooth the edge-shape behaviors in the log curve while preserving them is essential for recognizing the stratification changes. The second limitation is the generation of spurious oscillations in the reconstructed signals at the vicinity of the edges, known as the Gibbs phenomena. At the present study to overcome these problems, we use, for the first time, the inverse polynomial reconstruction method (IPRM) based on discrete cosine transform (DCT) to attenuate the random noise by projecting the data first on the orthogonal bases derived from Gegenbauer polynomials and then on cosine space. The de-noising level depends on the minimum description length (MDL) criterion that minimizes the distance of all the corresponding coefficients between the original and reconstructed signals during 100 iterations. However, in the proposed method, we use a genetic algorithm to decide quickly and optimally the coefficients of DCT and Gegenbauer required in real data reconstruction related to one of the oil fields in southwestern Iran. The results show that this preprocessing procedure compared to DCT and DWT, increases the success rate of the SOM method for oil-bearing zone detection based on evidence such as core drilling and perforation data. At the same time, the computational time is much shorter than the usual IPRM. The optimal perforation subsequent to this study may cut unwanted water production from adjacent layers. Such water with radioactive nature, in addition to causing environmental problems, greatly increases the cost of extraction operations due to the need to install surface-separating equipment.

Spectrally Sparse Tensor Reconstruction in Optical Coherence Tomography Using Nuclear Norm Penalisation

Reconstruction of 3D objects in various tomographic measurements is an important problem which can be naturally addressed within the mathematical framework of 3D tensors. In Optical Coherence Tomography, the reconstruction problem can be recast as a tensor completion problem. Following the seminal work of Candès et al., the approach followed in the present work is based on the assumption that the rank of the object to be reconstructed is naturally small, and we leverage this property by using a nuclear norm-type penalisation. In this paper, a detailed study of nuclear norm penalised reconstruction using the tubal Singular Value Decomposition of Kilmer et al. is proposed. In particular, we introduce a new, efficiently computable definition of the nuclear norm in the Kilmer et al. framework. We then present a theoretical analysis, which extends previous results by Koltchinskii Lounici and Tsybakov. Finally, this nuclear norm penalised reconstruction method is applied to real data reconstruction experiments in Optical Coherence Tomography (OCT). In particular, our numerical experiments illustrate the importance of penalisation for OCT reconstruction.

Simultaneous maximum a posteriori longitudinal PET image reconstruction

Positron emission tomography (PET) is frequently used to monitor functional changes that occur over extended time scales, for example in longitudinal oncology PET protocols that include routine clinical follow-up scans to assess the efficacy of a course of treatment. In these contexts PET datasets are currently reconstructed into images using single-dataset reconstruction methods. Inspired by recently proposed joint PET-MR reconstruction methods, we propose to reconstruct longitudinal datasets simultaneously by using a joint penalty term in order to exploit the high degree of similarity between longitudinal images. We achieved this by penalising voxel-wise differences between pairs of longitudinal PET images in a one-step-late maximum a posteriori (MAP) fashion, resulting in the MAP simultaneous longitudinal reconstruction (SLR) method. The proposed method reduced reconstruction errors and visually improved images relative to standard maximum likelihood expectation-maximisation (ML-EM) in simulated 2D longitudinal brain tumour scans. In reconstructions of split real 3D data with inserted simulated tumours, noise across images reconstructed with MAP-SLR was reduced to levels equivalent to doubling the number of detected counts when using ML-EM. Furthermore, quantification of tumour activities was largely preserved over a variety of longitudinal tumour changes, including changes in size and activity, with larger changes inducing larger biases relative to standard ML-EM reconstructions. Similar improvements were observed for a range of counts levels, demonstrating the robustness of the method when used with a single penalty strength. The results suggest that longitudinal regularisation is a simple but effective method of improving reconstructed PET images without using resolution degrading priors.

Sub-nanometer resolution XPS depth profiling: Sensing of atoms

The development of a method capable of distinguishing a single atom in a single molecule is important in many fields. The results reported herein demonstrate sub-nanometer resolution for angularly resolved X-ray photoelectron spectroscopy (ARXPS). This is made possible by the incorporation of a Maximum Entropy Method (MEM) model, which utilize density corrected electronic emission factors to the X-ray photoelectron spectroscopy (XPS) experimental results.In this paper we report on the comparison between experimental ARXPS results and reconstructed for both inorganic and organic thin film samples. Unexpected deviations between experimental data and calculated points are explained by the inaccuracy of the constants and standards used for the calculation, e.g. emission factors, scattering intensity and atomic density through the studied thickness.The positions of iron, nitrogen and fluorine atoms were determined in the molecules of the studied self-assembled monolayers. It has been shown that reconstruction of real spectroscopic data with 0.2nm resolution is possible.

A two-step filtering-based iterative image reconstruction method for interior tomography.

The optimization-based method that utilizes the additional sparse prior of region-of-interest (ROI) image, such as total variation, has been the subject of considerable research in problems of interior tomography reconstruction. One challenge for optimization-based iterative ROI image reconstruction is to build the relationship between ROI image and truncated projection data. When the reconstruction support region is smaller than the original object, an unsuitable representation of data fidelity may lead to bright truncation artifacts in the boundary region of field of view. In this work, we aim to develop an iterative reconstruction method to suppress the truncation artifacts and improve the image quality for direct ROI image reconstruction. A novel reconstruction approach is proposed based on an optimization problem involving a two-step filtering-based data fidelity. Data filtering is achieved in two steps: the first takes the derivative of projection data; in the second step, Hilbert filtering is applied in the differentiated data. Numerical simulations and real data reconstructions have been conducted to validate the new reconstruction method. Both qualitative and quantitative results indicate that, as theoretically expected, the proposed method brings reasonable performance in suppressing truncation artifacts and preserving detailed features. The presented local reconstruction method based on the two-step filtering strategy provides a simple and efficient approach for the iterative reconstruction from truncated projections.

Dynamic relaxation in algebraic reconstruction technique (ART) for breast tomosynthesis imaging

Background and objectivesA major challenge in Digital Breast Tomosynthesis (DBT) is handling image noise since the 3D reconstructed images are obtained from low dose projections and limited angular range. The use of the iterative reconstruction algorithm Algebraic Reconstruction Technique (ART) in clinical context depends on two key factors: the number of iterations needed (time consuming) and the image noise after iterations. Both factors depend highly on a relaxation coefficient (λ), which may give rise to slow or noisy reconstructions, when a single λ value is considered for the entire iterative process. The aim of this work is to present a new implementation for the ART that takes into account a dynamic mode to calculate λ in DBT image reconstruction. MethodsA set of initial reconstructions of real phantom data was done using constant λ values. The results were used to choose, for each iteration, the suitable λ value, taking into account the image noise level and the convergence speed. A methodology to optimize λ automatically during the image reconstruction was proposed. ResultsResults showed we can dynamically choose λ values in such a way that the time needed to reconstruct the images can be significantly reduced (up to 70%) while achieving similar image quality. These results were confirmed with one clinical dataset. ConclusionsWith simple methodology we were able to dynamically choose λ in DBT image reconstruction with ART, allowing a shorter image reconstruction time without increasing image noise.

Iterative Image Reconstruction for Limited-Angle CT Using Optimized Initial Image.

Limited-angle computed tomography (CT) has great impact in some clinical applications. Existing iterative reconstruction algorithms could not reconstruct high-quality images, leading to severe artifacts nearby edges. Optimal selection of initial image would influence the iterative reconstruction performance but has not been studied deeply yet. In this work, we proposed to generate optimized initial image followed by total variation (TV) based iterative reconstruction considering the feature of image symmetry. The simulated data and real data reconstruction results indicate that the proposed method effectively removes the artifacts nearby edges.

Status and Future Evolution of the ATLAS Offline Software

These proceedings give a summary of the many software upgrade projects undertaken to prepare ATLAS for the challenges of Run-2 of the LHC. Those projects include a significant reduction of the CPU time required for reconstruction of real data with high average pile-up event rates compared to 2012. This is required to meet the challenges of the expected increase in pileup and the higher data taking rate of up to 1 kHz. By far the most ambitious project is the implementation of a completely new analysis model, based on a new ROOT readable reconstruction format, xAOD. The new model also includes a reduction framework based on a train model to centrally produce skimmed data samples and an analysis framework. These proceedings close with a brief overview of future software projects and plans that will lead up to the coming Long Shutdown 2 as the next major ATLAS software upgrade phase.

Line Integral Alternating Minimization Algorithm for Dual-Energy X-Ray CT Image Reconstruction.

We propose a new algorithm, called line integral alternating minimization (LIAM), for dual-energy X-ray CT image reconstruction. Instead of obtaining component images by minimizing the discrepancy between the data and the mean estimates, LIAM allows for a tunable discrepancy between the basis material projections and the basis sinograms. A parameter is introduced that controls the size of this discrepancy, and with this parameter the new algorithm can continuously go from a two-step approach to the joint estimation approach. LIAM alternates between iteratively updating the line integrals of the component images and reconstruction of the component images using an image iterative deblurring algorithm. An edge-preserving penalty function can be incorporated in the iterative deblurring step to decrease the roughness in component images. Images from both simulated and experimentally acquired sinograms from a clinical scanner were reconstructed by LIAM while varying the regularization parameters to identify good choices. The results from the dual-energy alternating minimization algorithm applied to the same data were used for comparison. Using a small fraction of the computation time of dual-energy alternating minimization, LIAM achieves better accuracy of the component images in the presence of Poisson noise for simulated data reconstruction and achieves the same level of accuracy for real data reconstruction.

Self-adapting denoising, alignment and reconstruction in electron tomography in materials science

An automatic procedure for electron tomography is presented. This procedure is adapted for specimens that can be fashioned into a needle-shaped sample and has been evaluated on inorganic samples. It consists of self-adapting denoising, automatic and accurate alignment including detection and correction of tilt axis, and 3D reconstruction. We propose the exploitation of a large amount of information of an electron tomography acquisition to achieve robust and automatic mixed Poisson–Gaussian noise parameter estimation and denoising using undecimated wavelet transforms. The alignment is made by mixing three techniques, namely (i) cross-correlations between neighboring projections, (ii) common line algorithm to get a precise shift correction in the direction of the tilt axis and (iii) intermediate reconstructions to precisely determine the tilt axis and shift correction in the direction perpendicular to that axis. Mixing alignment techniques turns out to be very efficient and fast. Significant improvements are highlighted in both simulations and real data reconstructions of porous silicon in high angle annular dark field mode and agglomerated silver nanoparticles in incoherent bright field mode. 3D reconstructions obtained with minimal user-intervention present fewer artefacts and less noise, which permits easier and more reliable segmentation and quantitative analysis. After careful sample preparation and data acquisition, the denoising procedure, alignment and reconstruction can be achieved within an hour for a 3D volume of about a hundred million voxels, which is a step toward a more routine use of electron tomography.

TIMBIR: A Method for Time-Space Reconstruction From Interlaced Views

Synchrotron X-ray computed tomography (SXCT) is increasingly being used for 3-D imaging of material samples at micron and finer scales. The success of these techniques has increased interest in 4-D reconstruction methods that can image a sample in both space and time. However, the temporal resolution of widely used 4-D reconstruction methods is severely limited by the need to acquire a very large number of views for each reconstructed 3-D volume. Consequently, the temporal resolution of current methods is insufficient to observe important physical phenomena. Furthermore, measurement nonidealities also tend to introduce ring and streak artifacts into the 4-D reconstructions. In this paper, we present a time-interlaced model-based iterative reconstruction (TIMBIR) method, which is a synergistic combination of two innovations. The first innovation, interlaced view sampling, is a novel method of data acquisition, which distributes the view angles more evenly in time. The second innovation is a 4-D model-based iterative reconstruction algorithm (MBIR), which can produce time-resolved volumetric reconstruction of the sample from the interlaced views. In addition to modeling both the sensor noise statistics and the 4-D object, the MBIR algorithm also reduces ring and streak artifacts by more accurately modeling the measurement nonidealities. We present reconstructions of both simulated and real X-ray synchrotron data, which indicate that TIMBIR can improve temporal resolution by an order of magnitude relative to existing approaches.

A shape-based quality evaluation and reconstruction method for electrical impedance tomography

Linear methods of reconstruction play an important role in medical electrical impedance tomography (EIT) and there is a wide variety of algorithms based on several assumptions. With the Graz consensus reconstruction algorithm for EIT (GREIT), a novel linear reconstruction algorithm as well as a standardized framework for evaluating and comparing methods of reconstruction were introduced that found widespread acceptance in the community.In this paper, we propose a two-sided extension of this concept by first introducing a novel method of evaluation. Instead of being based on point-shaped resistivity distributions, we use 2759 pairs of real lung shapes for evaluation that were automatically segmented from human CT data. Necessarily, the figures of merit defined in GREIT were adjusted. Second, a linear method of reconstruction that uses orthonormal eigenimages as training data and a tunable desired point spread function are proposed.Using our novel method of evaluation, this approach is compared to the classical point-shaped approach. Results show that most figures of merit improve with the use of eigenimages as training data. Moreover, the possibility of tuning the reconstruction by modifying the desired point spread function is shown. Finally, the reconstruction of real EIT data shows that higher contrasts and fewer artifacts can be achieved in ventilation- and perfusion-related images.

Structured Models for Cell Populations: Direct and Inverse Problems

Structured population models in biology lead to integro-differential equations that describe the evolution in time of the population density taking into account a given feature such as the age, the size, or the volume. These models possess interesting ana- lytic properties and have been used extensively in a number of areas. In this article, we consider a size-structured model for cell division and revisit the question of determining the division (birth) rate from the measured stable size distribution of the population. We formulate such question as an inverse problem for an integro-differential equation posed on the half line. The focus is to compare from a computational view point the perfor- mance of different algorithms. Finally, we will discuss real data reconstructions with E. coli data.