Accuracy Of Tomographic Reconstruction Research Articles

Camera spatial arrangement influence on reconstruction accuracy of chemiluminescence tomography.

Computed tomography of chemiluminescence (CTC) has been demonstrated to be a powerful tool for three-dimensional (3D) combustion visualization and measurement, in which the number of cameras and their spatial arrangement significantly impact the tomographic reconstruction quality. In this work, the relationship of the camera spatial arrangement and tomographic reconstruction accuracy is theoretically established based on two-dimensional (2D) and 3D Mojette transforms and their accurate reconstruction conditions. Numerical simulations and experiments were conducted to demonstrate the theories. The results suggest that the exact reconstruction conditions of the Mojette transforms can be used to determine the minimum number of cameras required for tomography reconstruction, and its achieved reliability can be used as an indicator to predict the reconstruction quality. Besides, the 2D coplanar semicircular configuration exhibits a better performance than that of the 3D non-coplanar arrangement. When the 3D non-coplanar arrangement is adopted, the cameras should be widely distributed in the hemispherical space. The related research provides a theoretical basis for the establishment of the CTC system and other tomography modalities.

Noise Considerations for Tomographic Reconstruction of Single-Projection Digital Holographic Interferometry-Based Radiation Dosimetry

Optical Calorimetry (OC) is a 2D Digital Holographic Interferometry (DHI)-based measurement technique with potential applications for the 3D dosimetry of ultra-high dose rate (FLASH) radiation therapy beams through tomographic reconstruction. This application requires accurate measurements of DHI signals in environments with low signal-to-noise ratios (SNRs) in order to accurately measure absorbed energy to a medium per unit mass (Dose). However, tomographic reconstruction accuracy is sensitive to noise in the measurements. In this study, a virtual model of an OC dosimeter was used to characterize and model major sources of noise within a DHI setup, allowing for the modelled noise sources to be selectively reduced. The tomographic reconstruction of the 3D dose distribution was achieved using the inverse Abel transform. Reducing the noise contribution from atmospheric turbulence and mechanical vibration by one half improved the central axis reconstruction error from 6.5% to 1.3% and 1.1%, respectively, and the mean dose difference from 2.9% to 0.4% and 0.3%, respectively. This indicates the potential of the tomographic DHI-based 3D OC dosimeter to reconstruct accurate 3D dose distributions from a single projection if the specified sources of noise can be reduced to acceptable levels. The used methodology is applicable to any application of tomographic DHI where reconstruction quality is highly sensitive to noise.

Tomography of the Solar Corona with the Metis Coronagraph I: Predictive Simulations with Visible-Light Images

The Solar Orbiter/Metis coronagraph records full-Sun visible-light polarized brightness (\(pB\)-) images of the solar corona. This work investigates the utility of a synoptic observational program of Metis for tomographic reconstruction of the three-dimensional (3D) distribution of the electron density of the global solar corona. During its lifetime, the mission’s distance to the Sun will range over \(\approx 0.3-1.0\,\mathrm{AU}\), while its solar latitude will span \(\approx \pm 33^{\circ}\). The limitations that this orbital complexity poses on tomographic reconstructions are explored in this work. Using the predicted orbital information of Solar Orbiter and 3D-MHD simulations of the solar corona using the Alfvén Wave Solar atmosphere Model (AWSoM), time series of synthetic Metis \(pB\)-images were computed and used as data to attempt tomographic reconstruction of the model. These numerical experiments were implemented for solar-minimum and solar-maximum conditions. In both cases, images were synthesized from three orbital segments, corresponding to extreme geometrical conditions of observation by Metis: aphelion, perihelion, and maximum solar latitude. The range of heights that can be reconstructed, the required data-gathering period, and the accuracy of the reconstruction, are discussed in detail for each case. As a general conclusion, a Metis synoptic observational program with a cadence of at least four images day−1 provides enough data to attempt tomographic reconstructions during the whole lifetime of the mission, a requirement well within the two- to three-hour cadence of the current synoptic program. This program will allow implementation of tomography experimenting with different values for the cadence of the time series of images used to feed reconstructions. Its cadence will also provide continuous opportunities to select images avoiding highly dynamic events, which compromise the accuracy of tomographic reconstructions.

Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized least squares (LSQ) approach and a compressive sensing (CS) approach for water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) slant wet delay (SWD) estimates. In this context, the term “observing geometry” mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are (1) the comparison of the observing geometry's effects on the tomographic reconstruction accuracy when using LSQ or CS for the solution of the tomographic system and (2) the investigation of the effect of the signal directions' variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS intersite distance represents a good rule of thumb for both LSQ- and CS-based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available.

Assessing the Spatial Accuracy of the Reconstruction in Atom Probe Tomography and a New Calibratable Adaptive Reconstruction.

We define a measure for the accuracy of tomographic reconstruction in atom probe tomography, named here the spatial error index. We demonstrate that this index can be used to compare rigorously the spatial accuracy of various different approaches to the calculation of tomographic reconstruction. This is useful, for example, to evaluate the performance of alternate tomographic reconstruction approaches, and ensures that the comparisons are independent of individual data quality or other instrumental parameters. We then introduce a new "adaptive reconstruction" formalism that uses a progression of reconstruction parameters based on a per-atom correction from the cube root of the inverse of the voltage, along with linear correction factors linked to the evaporation sequence. We apply the measure for spatial accuracy to this new reconstruction protocol.

Syris: a flexible and efficient framework for X-ray imaging experiments simulation

An open-source framework for conducting a broad range of virtual X-ray imaging experiments, syris, is presented. The simulated wavefield created by a source propagates through an arbitrary number of objects until it reaches a detector. The objects in the light path and the source are time-dependent, which enables simulations of dynamic experiments, e.g. four-dimensional time-resolved tomography and laminography. The high-level interface of syris is written in Python and its modularity makes the framework very flexible. The computationally demanding parts behind this interface are implemented in OpenCL, which enables fast calculations on modern graphics processing units. The combination of flexibility and speed opens new possibilities for studying novel imaging methods and systematic search of optimal combinations of measurement conditions and data processing parameters. This can help to increase the success rates and efficiency of valuable synchrotron beam time. To demonstrate the capabilities of the framework, various experiments have been simulated and compared with real data. To show the use case of measurement and data processing parameter optimization based on simulation, a virtual counterpart of a high-speed radiography experiment was created and the simulated data were used to select a suitable motion estimation algorithm; one of its parameters was optimized in order to achieve the best motion estimation accuracy when applied on the real data. syris was also used to simulate tomographic data sets under various imaging conditions which impact the tomographic reconstruction accuracy, and it is shown how the accuracy may guide the selection of imaging conditions for particular use cases.

Optoacoustic image segmentation based on signal domain analysis

Efficient segmentation of optoacoustic images has importance in enhancing the diagnostic and quantification capacity of this modality. It may also aid in improving the tomographic reconstruction accuracy by accounting for heterogeneous optical and acoustic tissue properties. In particular, when imaging through complex biological tissues, the real acoustic properties often deviate considerably from the idealized assumptions of homogenous conditions, which may lead to significant image artifacts if not properly accounted for. Although several methods have been proposed aiming at estimating and accounting for the complex acoustic properties in the image domain, accurate delineation of structures is often hindered by low contrast of the images and other artifacts produced due to incomplete tomographic coverage and heuristic assignment of the tissue properties during the reconstruction process. In this letter, we propose instead a signal domain analysis approach that retrieves acoustic properties of the object to be reconstructed from characteristic features of the detected optoacoustic signals prior to image reconstruction. Performance of the proposed method is first tested in simulation and experiment using two-dimensional tissue-mimicking phantoms. Significant improvements in the segmentation abilities and overall reconstructed image quality are further showcased in experimental cross-sectional data acquired from a human finger.

Computational and experimental study on accuracy of off-axis reconstructions in optical diffraction tomography

We present a study on spatial changes in the accuracy of tomographic reconstructions obtained with two of the most popular tomographic reconstruction algorithms for diffraction tomography—filtered backprojection (FBPJ) and Rytov-based filtered backpropagation (FBPP). We find out that not only FBPJ but also FBPP suffers from a significant loss of accuracy in the off-axis regions of a tomographic reconstruction and this effect is stronger for objects with a high refractive index contrast. Moreover, we propose some modifications to FBPP which allow for significant improvement of the off-axis performance of the algorithm. In the modified algorithm, called the extended depth of focus filtered backpropagation (EDOF-FBPP), scattered waves are backpropagated using a rigorous propagation algorithm, and then the Rytov approximation is applied on extended EDOF images. This modification (1) prevents violation of the Rytov validity condition due to the defocus of scattered waves and (2) suppresses unwrapping errors. The tomographic reconstruction algorithms FBPJ, FBPP, and EDOF-FBPP are extensively tested with numerical simulations supported with rigorous wave scattering methods. The experimental evaluation of the performance of the tomographic algorithms is provided with a tomographic measurement of an optical microtip located 21 μ m from the central axis of the reconstruction.

Spatial filtering improved tomographic PIV

Tomographic reconstruction accuracy is of fundamental importance to obtain reliable three-dimen- sional three-components velocity field measurements when implementing tomographic particle image velocimetry. Algebraic methods (Herman and Lent 1976) are quite well established to handle the problem in case of high spatial frequency spots on a dark background imaged by a limited number of simultaneous views; however, their efficacy is limited in case of dense distributions to be reconstructed. In the present work, an easy implementable modified version of the commonly used multiplicative algebraic recon- struction technique is proposed, allowing a remarkable improvement of the tomographic reconstruction quality only slightly increasing the computational cost. The tech- nique is based on artificial diffusion applied by Gaussian smoothing after each iteration of the reconstruction pro- cedure. Numerical simulations show that the increase in the reconstruction quality leads to a significant reduction of the modulation effects in the velocity measurement due to the coherent ghost particles motion. An experimental applica- tion in fractal grid turbulence highlights an improvement of the signal strength and a reduction of the uncertainty in the velocity measurement.

Numerical model for tomographic image formation in transmission x-ray microscopy

We present a numerical image-formation model for investigating the influence of partial coherence, sample thickness and depth-of-focus on the accuracy of tomographic reconstructions in transmission x-ray microscopes. The model combines wave propagation through the object by finite difference techniques with Fourier methods. We include a ray-tracing model to analyse the origin of detrimental stray light in zone plate-based x-ray microscopes. These models allow optimization of x-ray microscopy systems for quantitative tomographic imaging of thick objects. Results show that both the depth-of-focus and the reconstructed local absorption coefficient are highly dependent on the degree of coherence of the optical system.

A parametric model for fusion neutron emissivity tomography for the KN3 neutron camera at JET

A parametric model for fusion neutron emissivity is presented and applied to the KN3 neutron camera data collected during the trace tritium experiment at the Joint European Torus. This work is aimed at achieving a good compromise between accuracy of tomographic reconstruction and low model complexity. This means low numerical degeneracy and good time consistency of the results. The model is compared both with plasma simulation codes and other tomographic techniques, which use KN3 line integrated emissivity data, showing good agreement over the entire data set analysed (≈500 plasma discharges).

Optimal experiment design for time-lapse traveltime tomography

Geophysical monitoring techniques offer the only noninvasive approach capable of assessing the spatial and temporal dynamics of subsurface fluid processes. Increasingly, permanent sensor arrays in boreholes and on the ocean floor are being deployed to improve the repeatability and increase the temporal sampling of monitoring surveys. Because permanent arrays require a large up-front capital investment and are difficult (or impossible) to reconfigure once installed, a premium is placed on selecting a geometry capable of imaging the desired target at minimum cost. We have taken a simple approach to optimizing downhole sensor configurations for monitoring experiments making use of differential seismic traveltimes. We used a design quality metric based on the accuracy of tomographic reconstructions for a suite of imaging targets. By not requiring an explicit singular value decomposition of the forward operator, evaluation of this objective function scaled to problems with a large numberof unknowns. We restricted the design problem by recasting the array geometry into a low-dimensional form more suitable for optimization at a reasonable computational cost. We tested two search algorithms on the design problem: the Nelder-Mead downhill simplex method and the multilevel coordinate search algorithm. The algorithm was tested for four crosswell acquisition scenarios relevant to continuous seismic monitoring, a two-parameter array optimization, several scenarios involving four-parameter length/offset optimizations, and a comparison of optimal multisource designs. In the last case, we also examined trade-offs between source sparsity and the quality of tomographic reconstructions. Asymmetrical array lengths improved localized image quality in crosswell experiments with a small number of sources and a large number of receivers. Preliminary results also suggested that high-quality differential images could be generated using only a small number of optimally positioned sources in tandem with a more extensive receiver array.

Choice of measurement sets in qubit tomography

Optimal generalized measurements for state estimation are well understood. However, practical quantum state tomography is typically performed using a fixed set of projective measurements, and the question of how to choose these measurements has been largely unexplored in the literature. In this work, we develop theoretical asymptotic bounds for the average fidelity of pure qubit tomography using measurement sets whose axes correspond to faces of Platonic solids. We also present comprehensive simulations of maximum likelihood tomography for mixed qubit states using the Platonic solid measurements. We show that overcomplete measurement sets can be used to improve the accuracy of tomographic reconstructions.