Simultaneous Algebraic Reconstruction Technique Algorithm Research Articles

Enhanced image reconstruction of electrical impedance tomography using simultaneous algebraic reconstruction technique and K-means clustering

<span lang="EN-US">Electrical impedance tomography (EIT), as a non-ionizing tomography method, has been widely used in various fields of application, such as engineering and medical fields. This study applies an iterative process to reconstruct EIT images using the simultaneous algebraic reconstruction technique (SART) algorithm combined with K-means clustering. The reconstruction started with defining the finite element method (FEM) model and filtering the measurement data with a Butterworth low-pass filter. The next step is solving the inverse problem in the EIT case with the SART algorithm. The results of the SART algorithm approach were classified using the K-means clustering and thresholding. The reconstruction results were evaluated with the peak signal noise ratio (PSNR), structural similarity indices (SSIM), and normalized root mean square error (NRMSE). They were compared with the one-step gauss-newton (GN) and total variation regularization based on iteratively reweighted least-squares (TV-IRLS) methods. The evaluation shows that the average PSNR and SSIM of the proposed reconstruction method are the highest of the other methods, each being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest, which is 0.04. The performance evaluation also shows that the proposed method is faster than the other methods.</span>

Improving Gas Distribution Tomography: Evaluation of Algorithms and Fan-Beam Measurement Geometries Indoors

Gas distribution mapping (GDM) is widely used to evaluate the efficiency of environmental control systems and locate pollution sources. In this study, the optimized fan-beam measurement geometries and tomography algorithms were proposed for mapping the gas distributions indoors accurately. The airflow and gas distributions in two different types of rooms were predicted using the validated computational fluid dynamics (CFD) method. Then, the performances of five algorithms, including iterative and Tikhonov regularization methods, were quantitatively compared, and the measurement geometries (i.e., number of optical paths, grid resolution, and laser emitter amount) were investigated. The effect of measurement noise on reconstruction accuracy was also analyzed. The results have shown that the simultaneous algebraic reconstruction technique (SART) and the maximum likelihood expectation maximization (MLEM) algorithms were better than the other algorithms, and their averaged relative root mean square errors (RRMSEs) were at least 39% lower with the iteration residual of 0.0015. The optimum grid resolution and the number of optical paths were <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times20$ </tex-math></inline-formula> grid cells and 200 paths, respectively. In addition, the sensitivity of the SART and MLEM algorithms to noise was almost the same, and the addition of the laser emitter could improve the reconstruction accuracy of most cases in this study.

Optimization of a customized simultaneous algebraic reconstruction technique algorithm for phase-contrast breast computed tomography

Objective. To introduce the optimization of a customized GPU-based simultaneous algebraic reconstruction technique (cSART) in the field of phase-contrast breast computed tomography (bCT). The presented algorithm features a 3D bilateral regularization filter that can be tuned to yield optimal performance for clinical image visualization and tissues segmentation. Approach. Acquisitions of a dedicated test object and a breast specimen were performed at Elettra, the Italian synchrotron radiation (SR) facility (Trieste, Italy) using a large area CdTe single-photon counting detector. Tomographic images were obtained at 5 mGy of mean glandular dose, with a 32 keV monochromatic x-ray beam in the free-space propagation mode. Three independent algorithms parameters were optimized by using contrast-to-noise ratio (CNR), spatial resolution, and noise texture metrics. The results obtained with the cSART algorithm were compared with conventional SART and filtered back projection (FBP) reconstructions. Image segmentation was performed both with gray scale-based and supervised machine-learning approaches. Main results. Compared to conventional FBP reconstructions, results indicate that the proposed algorithm can yield images with a higher CNR (by 35% or more), retaining a high spatial resolution while preserving their textural properties. Alternatively, at the cost of an increased image ‘patchiness’, the cSART can be tuned to achieve a high-quality tissue segmentation, suggesting the possibility of performing an accurate glandularity estimation potentially of use in the realization of realistic 3D breast models starting from low radiation dose images. Significance. The study indicates that dedicated iterative reconstruction techniques could provide significant advantages in phase-contrast bCT imaging. The proposed algorithm offers great flexibility in terms of image reconstruction optimization, either toward diagnostic evaluation or image segmentation.

A Fast Tomographic Reconstruction Method for Flame Temperature Distribution Measurement Based on Direct Solution Algorithm

The rapid and accurate measurement of the flame temperature distribution is of great significance to the structural design and health diagnosis of the engine. Aiming at the low reconstruction efficiency of traditional flame temperature distribution reconstruction algorithms, a Direct Solution algorithm for flame temperature distribution reconstruction is proposed in this paper based on the structural characteristics of the reconstruction equations. By setting several numerical cases, the performance of the Direct Solution algorithm and some commonly used traditional algorithms, such as Simultaneous Algebraic Reconstruction Technique (SART), Least Squares QR-factorization (LSQR) algorithm, Non-Negative Least Squares QR-factorization (NNLS) algorithm, is compared in the reconstruction of the flame temperature distribution. The results show that the efficiency of the Direct Solution method is 169.4, 7.4, and 3483.3 times higher than that of the SART, LSQR, and NNLS algorithms under the condition of 40 × 40 grids. In addition, with the increase of the number of grids, the growth rate of the reconstruction time of the Direct Solution algorithm is much lower than that of other algorithms. The overall reconstruction accuracy of the Direct Solution algorithm is better than that of SART and LSQR algorithms. This shows that it has an excellent comprehensive performance and has a great application prospect in the rapid reconstruction of the temperature distribution.

Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms.

Effective identification of pollution sources is particularly important for indoor air quality. Accurate estimation of source strength is the basis for source effective identification. This paper proposes an optimization method for the deconvolution process in the source strength inverse calculation. In the scheme, the concept of time resolution was defined, and combined with different filtering positions and filtering algorithms. The measures to reduce effects of measurement noise were quantitatively analyzed. Additionally, the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and scored. The hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative methods. Results showed that for the filtering position and algorithm, Butterworth filtering performed better, and different filtering positions had little effect on the inverse calculation. For the calculation time step, the optimal Tr (time resolution) was 0.667% and 1.33% in the simulation and experiment, respectively. The hybrid algorithms were found to not perform better than the single algorithms, and the SART (simultaneous algebraic reconstruction technique) algorithm from CAT (computer assisted tomography) yielded better performances in the accuracy and stability of source strength identification. The relative errors of the inverse calculation for source strength were typically below 25% using the optimization scheme.

Assessment of imaging performance of digital breast tomosynthesis based on systematic simulation

To assess the imaging performance of digital breast tomography (DBT) based on systematic simulation. The raw measurements of physical phantoms at a variety of radiation dose levels and clinical patients at the normal radiation dose were acquired from a clinical DBT system for low-dose simulation and reconstruction using 3 reconstruction algorithms, namely Feldkamp-Davis-Kress (FDK), simultaneous algebraic reconstruction technique (SART) and adaptive steepest-descent projection onto convex sets with total-variation constraint (ASDPOCS-TV) algorithms. The image quality was compared across different radiation dose levels and reconstruction algorithms in terms of signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), noise-power spectrum (NPS), artifact spread function (ASF) and full width at half maximum (FWHM) of ASF indexes. The reliability of low-dose DBT simulation strategy was verified by the experiment. Within a suitable range of dose levels, increasing the doses resulted in reduced high-frequency noise component and significantly increased SNR (P < 0.05). But when the value of exposure was below 40 mAs, the images acquired at different dose levels had similar representation. The performance of the 3 reconstruction algorithms varied for different anatomical structures, and the image quality of ASDPOCS-TV algorithm was generally superior to SART and FDK algorithms with less through-plane artifacts and noise. The quality of DBT images is significantly affected by both radiation dose and reconstruction algorithms. A tradeoff of the parameters, the overall image quality and the clinical needs for diagnostic purposes should be considered to achieve the optimal imaging performance on a given clinical task.

Tomographic reconstruction of the runaway distribution function in TCV using multispectral synchrotron images

Synchrotron radiation observed in a quiescent Tokamak à Configuration Variable (TCV) runaway discharge is studied using filtered camera images targeting three distinct wavelength intervals. Through the tomographic simultaneous algebraic reconstruction technique (SART) procedure the high momentum, high pitch angle part of the spatial and momentum distribution of these relativistic particles is reconstructed. Experimental estimates of the distribution are important for verification and refinement of formation-, decay- and transport-models underlying runaway avoidance and mitigation strategy design. Using a test distribution it is demonstrated that the inversion procedure provides estimates accurate to within a few tens of percent in the region of phase-space contributing most to the synchrotron image. We find that combining images filtered around different parts of the emission spectrum widens the probed part of momentum-space and reduces reconstruction errors. Next, the SART algorithm is used to obtain information on the spatiotemporal runaway momentum distribution in a selected TCV discharge. The momentum distribution is found to relax towards an avalanche-like exponentially decaying profile. Anomalously high pitch angles and a radial profile increasing towards the edge are found for the most strongly emitting particles in the distribution.Pitch angle scattering by toroidal magnetic field ripple is consistent with this picture. An alternative explanation is the presence of high frequency instabilities in combination with the formation of a runaway shell at the edge of the plasma.

Experimental demonstration of three-dimensional high energy electron radiography

High energy electron radiography (HEER) proposed first for real-time high spatial and temporal resolution diagnosis of warm dense matter (WDM) and inertial confinement fusion (ICF) has proved experimentally feasible for mesoscale sciences diagnosis. Until now, the spatial resolution of the images close to 1 μm has been reached experimentally which is better than that of X-rays and neutron radiography. However, traditional HEER obtains two-dimensional images which cannot accurately present the three-dimensional structure of the sample. To further improve the capability of HEER to diagnose and obtain the internal information of samples, three-dimensional high energy electron radiography (TDHEER) was put forward by combining HEER with three-dimensional (3D) reconstruction tomography technology. The validity and usage of the TDHEER method have been confirmed through simulation of the fully 3D diagnostic of static mesoscale sample. This paper focuses mainly on the experimental demonstration of the 3D high energy electron radiography. The feasibility of TDHEER is for the first time confirmed by the results achieved with different 3D reconstruction algorithms. The 3D reconstruction algorithms, analytical algorithm-filtered back projection (FBP), iterative algorithms-algebraic reconstruction technique (ART), and simultaneous algebraic reconstruction technique (SART) are used here. In this experiment, the less projected data are used, so it takes the less time to obtain two-dimensional (2D) HEER images and the reconstruction. In order to spend the time as little as possible and obtain the satisfactory quality of reconstruction result, there are three groups of projected image sets, 180, 36 and 18, acquired in our experiment. When all three algorithms are adopted in 180 projected images, the reconstructed images show that all three algorithms FBP, ART and SART are feasible for TDHEER. The different reconstructed slice images of the sample in &lt;i&gt;X-&lt;/i&gt;, &lt;i&gt;Y-&lt;/i&gt;, and &lt;i&gt;Z-&lt;/i&gt; direction clearly show the detailed structure of the sample. The images reconstructed by ART and SART algorithm are equivalent. Comparing with ART and SART, the reconstruction results by FBP can show more details, but there are some artifacts. Because the 36 2D HEER images fail to satisfy the Nyquist sampling theory, the analytic algorithm FBP is not used. Taking the result of FBP reconstructed by 180 images as a standard reference to compare the result of ART with the results of SART, the images reconstructed by the SART algorithm are closer to the original images. Testing 18 images, the results of the ART and SART both have lots of artifacts but the SART algorithm spends less time in reconstruction. As fewer projected images are used, more artifacts are found in the reconstructed images. Therefore, it is advantageous to combine the SART algorithm with 36 HEER projected images, which obtains high-quality reconstruction images and spends less time. The feasibility of TDHEER is confirmed experimentally for the first time and all three dimensions of the sample structures are obtained. Of the three different 3D reconstruction algorithms, the SART algorithm is the most suitable for reconstructing the few-view images. The TDHEER technology will extend HEER’s application fields, especially for mesoscale sciences.

Simultaneous correction of sensitivity and spatial resolution in projection-based magnetic particle imaging.

The purpose of this study was to develop a method to simultaneously correct the spatial resolution and inhomogeneous sensitivity of a receiving coil in projection-based magnetic particle imaging - and to investigate its efficacy through simulation and experimental studies. Magnetic particle imaging (MPI)images were reconstructed using the simultaneous algebraic reconstruction technique (SART), and simultaneous corrections to sensitivity and spatial resolution were performed by incorporating the sensitivity map of the receiving coil and the system function into the SART algorithm. After each SART update, the regularization method - with total variation (TV) minimization - was used to suppress noise amplification and artifact generation. For comparison, MPI images were also reconstructed using the filtered backprojection (FBP) method and the FBP-truncated singular value decomposition (TSVD) method, in which the system function was deconvolved from the projection data using TSVD. In simulation studies, the sensitivity map of a second-order, gradiometer-type receiving coil was generated using the Biot-Savart law, while the system function was obtained by calculating the MPI signals induced by magnetic nanoparticles at various distances from a field-free line (FFL), using a lock-in-amplifier model. The effects of a regularization parameter for TV minimization (α), number of iterations (N), and signal-to-noise ratio (SNR) of the MPI signals on the reconstructed MPI images of a numerical phantom were evaluated, using the image profiles and percent root mean square error (PRMSE). Experimental studies involved the calculation of the system function using a tube phantom. Projection data for an A-shaped phantom were acquired using our MPI scanner, and their MPI images were reconstructed from the projection data, as described above. When both the sensitivity and spatial resolution were corrected (SART-SR), the quality of the reconstructed images was seen to have improved, compared to when the spatial resolution was not corrected - or when the FBP and FBP-TSVD methods were used. When SNR was low (20), a larger α value yielded a better image. The minimum PRMSE occurred at N≈200-400 and increased with increasing N thereafter. When SNR was high (100), the image quality was generally not dependent on the α value within its studied range. The PRMSE decreased slowly with increasing N, and tended to converge to a constant value. The full width at half maximum (FWHM) of the profile was obtained from the A-shaped phantom, reconstructed using the SART-SR algorithm with α=0.05 and N=1000. The FWHM value of the tube (2mm diameter) in the A-shaped phantom image was found to be 2.2mm on average, whereas those calculated from the images obtained by the FBP and FBP-TSVD methods were 4.4 and 3.0mm on average, respectively. Spatial resolution improved when using the FBP-TSVD method as compared to the FBP method but image distortion and artifacts were observed. Although further studies are necessary to optimize the parameters used in the SART algorithm and in TV minimization, the present results suggest that the proposed method is useful for improving the image quality of projection-based MPI.

A SART algorithm for area integral model in desktop micro-CT system

Due to the finite size of detector unit, the area integral model (AIM) is closer to reality than the line integral model (LIM) when X-ray beams passing through a sample cast to CCD. A simultaneous algebraic reconstruction technique (SART) algorithm for AIM is proposed for improved image quality. First, a cone-beam to parallel-beam rearranged step was performed to simplify the model computation. Then, a judgement formula was used to quickly find the cells to be traversed by a strip-shaped X-ray beam and an effective method was presented to calculate intersection region. Via the comparison, both simulation and experiment show that this area-based SART has better reconstruction quality than traditional line-based SART. The algorithm is demonstrated the ability to micro-CT reconstruction. This AIM approach could be adapted for other iterative algorithms in fan-beam or small cone- angle beam CT geometry.

Sparse and limited-angle CT image reconstruction using dictionary learning constrained by L1 norm

Reconstructed images from computed tomography (CT) using the algebraic reconstruction technique (ART) and simultaneous ART (SART) algorithms often suffer from obvious artefacts when only sparse and limited-angle projection data are available. Using the ability of dictionary learning (DL) in image feature extraction and sparse signal representation, a new iterative reconstruction algorithm, ART-DL-L1, is proposed to overcome the aforementioned limitations. This new algorithm is based on DL and an L1 norm constraint, combined with ART. An alternate iterative solving strategy based on an approach of 'ART first, then adaptive dictionary learning' is suggested and is explicitly described in a flowchart depicting the ART-DL-L1 algorithm. For both a noisy projection of 360° sparse data and limitedangle data of 120°, simulation reconstruction results from the classic Shepp-Logan image obtained using ART-DL-L1 appear to be better than those obtained using SART and total variation (TV) algorithms and also better than the cutting-edge ART-DL-L2 algorithm. Five evaluation metrics corresponding to the root-mean-square error (RMSE), the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR), the residuals and the structural similarity (SSIM) index are adopted to estimate the reconstruction effect. The results suggest that the five metrics obtained using ART-DL-L1 outperform those obtained using the other three algorithms. The impact of using patches of various sizes played by the DL part in ART-DL-L1 is considered in the simulations and the patch size achieving the best reconstructed image quality is identified in this case as 25 (5 × 5). Overall, the proposed ART-DL-L1 algorithm may reduce artefacts and suppress noise from incomplete noisy projection CT imaging to some degree.

Experimental Research of In Vivo Mouse Cardiac 4D Micro-CT Imaging via Deformation Vector Field Registration

In the radiation therapy, high-quality four-dimensional (4D) Cone-beam Computed Tomography data of patients with lung cancer can be obtained by deforming prior images using estimated deformation vector fields (DVF). In pre-clinical field, meanwhile, four-dimensional Micro-Computed Tomography (4D Micro-CT) imaging is also a promising tool to explore long-term cardiopulmonary diseases via in vivo mouse models. However, since the respiration and heart rates of mice are 8 times faster than those of humans, and the higher resolution of Micro-CT even requires more projection views, the 4D Micro-CT imaging of mice is more challenging than that of human being. In this paper, we integrate the DVF registration method with retrospective gating technology, and try to realize cardiac 4D Micro-CT imaging of in vivo mouse. The whole experimental research is completed on our self-developed Micro-CT platform, and the procedures of implementation are as follows. Firstly, the multi-turn projection data through a specific scanning protocol is obtained, and then the projection views locating in the respiratory resting period are picked and categorized into different groups in terms of phase information acquired from the respiratory and ECG gating sub-system. Secondly, the selected projection data is reconstructed via the ordered subsets simultaneous algebraic reconstruction technique algorithm as prior images. Finally, the images within different cardiac phases are made by deforming the prior images using optimized DVF. The results show every one 4D Micro-CT image series with 5 cardiac phases can be secured through one scan, which all bear clear heart contours and are with less streak artifacts, and the images made by the proposed method have higher signal noise ratio compared with those by conventional algorithms. A similar performance can be obtained when the angular sampling interval decreases. Hence, the proposed 4D cardiac imaging technology can effectively characterize the cardiac motion, and can be developed as a professional tool to image the heart of a living mouse dynamically.

McSART: an iterative model-based, motion-compensated SART algorithm for CBCT reconstruction

4D cone beam computed tomography (CBCT) images of the thorax and abdomen can have reduced quality due to the limited number of projections per respiratory bin used in gated image reconstruction. In this work, we present a new algorithm to reconstruct high quality CBCT images by simultaneously reconstructing images and generating an associated respiratory motion model. This is done by updating model parameters to compensate for motion during the iterative image reconstruction process. CBCT image acquisition was simulated using the digital eXternal CArdiac Torso (XCAT) phantom, simulating breathing motion using four patient breathing traces. 4DCBCT images were reconstructed using the simultaneous algebraic reconstruction technique (SART), and compared to the proposed motion-compensated SART (McSART) algorithm. McSART used a motion model that describes tissue position as a function of diaphragm amplitude and velocity. The McSART algorithm alternately updated the motion model and image reconstruction, increasing the number of projections used for image reconstruction with every iteration. The model was able to interpolate and extrapolate deformations according to the magnitude of the surrogate signal. Without noise, the final iteration McSART images had HU errors at 31%, 34%, and 44% of their SART-reconstructed counterparts compared to ground truth XCAT images, with corresponding root-mean-square (RMS) motion model errors of 0.75 mm, 1.08 mm, and 1.17 mm respectively. With added image noise, McSART’s HU error was 31% of the SART-reconstructed 4DCBCT error, with a 1.43 mm RMS motion model error. Qualitatively, blurring and streaking artifacts were reduced in all the reconstructed images compared to 3D or SART-reconstructed 4DCBCT. The output of the algorithm was a high quality reference image and a corresponding motion model, that could be used to deform the reference image to any other point in a breathing cycle.

OA158] Noise equivalent number of quanta (NEQ): A tool for choosing the optimal reconstruction algorithm in computed tomography (CT)

Purpose Evaluate optimal reconstruction algorithm and voxel size for a breast CT system using NEQ. Methods CT scans of a wire phantom and a homogeneous cylindrical sample have been performed using a tomographic acquisition system for breast-CT with synchrotron radiation. By setting a monochromatic beam of 38 KeV , the images have been acquired using a CdTe photon-counting detector with a pixel size of 60 μ m. The optimal CT reconstruction algorithm has been investigated comparing the Filtered Back Projection (FBP), the Simultaneous Algebraic Reconstruction Technique (SART) and the Simultaneous Iterative Reconstruction Technique (SIRT). CT images have been reconstructed using two voxel sizes ( V 60 = 60 3 μ m3 and V 120 = 120 3 μ m3). The spatial resolution and the noise have been evaluated on CT reconstructions respectively trough the modulation transfer function (MTF) and the normalized noise power spectrum (NNPS). MTF has been evaluated with the thin wire method and the NNPS has been measured on the CT reconstructions of the homogeneous sample. The NEQ is defined as the ratio between the squared MTF and the NNPS. It is a measure of the signal to noise ratio in frequency domain and has been used to compare the performances of the considered reconstruction algorithms. Results For both used voxel sizes, compared to SART and FBP algorithms, SIRT algorithm shows highest values of NEQ for all accessible frequencies. Apart from the reconstruction algorithm, compared to V 60 , V 120 lead to better NEQ values for frequencies below 3.3 mm - 1 thus, for this system, details with diameter lower than 300 μ m are better resolved by the smallest voxel size available. Conclusions Summarizing information about spatial resolution and noise, NEQ is an extremely comprehensive metric which contains all the key issues for the global image quality assessment. For a given CT system, the comparison between NEQ curves measured on CT reconstructions provides a general method for choosing the best reconstruction algorithm and the voxel size to be used for achieving optimal performances in terms of spatial resolution and noise.

Fast implementation of area integral model SART algorithm based on look-up table

Forward projection and back-projection are two computationally intense operations in simultaneous algebraic reconstruction technique (SART), especially for area integral model (AIM). We have proposed a fast forward projection algorithm for AIM-based algebraic reconstruction technique, which can be used to accelerate the forward projection operation of area integral model SART. However, the back-projection operation is extremely time-consuming due to its complexity. In this paper, we propose a fast approach to improve the reconstruction speed of AIM-based SART algorithm. To avoid repeated computation of the intersection between rays and pixels, we construct three look up tables (LUTs) to store the number of rays that intersecting with a given pixel, and the corresponding ray indices and weighting factors, respectively, which can be done during the forward projection operation. In the back-projection operation, we update pixels value using these LUTs. On this basis, we utilize the multi-threading technique to implement the forward projection and back-projection operations on a multi-core platform. Experimental results show that the proposed LUT-based approach with multi-core acceleration achieves a speedup of 36.5 times over the conventional approach, and preserves accuracy of the results.

Improved Simultaneous Algebraic Reconstruction Technique Algorithm for Positron-Emission Tomography Image Reconstruction via Minimizing the Fast Total Variation

ContextThere has been considerable progress in the instrumentation for data measurement and computer methods for generating images of measured PET data. These computer methods have been developed to solve the inverse problem, also known as the “image reconstruction from projections” problem. AimIn this paper, we propose a modified Simultaneous Algebraic Reconstruction Technique (SART) algorithm to improve the quality of image reconstruction by incorporating total variation (TV) minimization into the iterative SART algorithm. MethodologyThe SART updates the estimated image by forward projecting the initial image onto the sinogram space. Then, the difference between the estimated sinogram and the given sinogram is back-projected onto the image domain. This difference is then subtracted from the initial image to obtain a corrected image. Fast total variation (FTV) minimization is applied to the image obtained in the SART step. The second step is the result obtained from the previous FTV update. The SART and the FTV minimization steps run iteratively in an alternating manner. Fifty iterations were applied to the SART algorithm used in each of the regularization-based methods. In addition to the conventional SART algorithm, spatial smoothing was used to enhance the quality of the image. All images were sized at 128 × 128 pixels. ResultsThe proposed algorithm successfully accomplished edge preservation. A detailed scrutiny revealed that the reconstruction algorithms differed; for example, the SART and the proposed FTV-SART algorithm effectively preserved the hot lesion edges, whereas artifacts and deviations were more likely to occur in the ART algorithm than in the other algorithms. ConclusionsCompared to the standard SART, the proposed algorithm is more robust in removing background noise while preserving edges to suppress the existent image artifacts. The quality measurements and visual inspections show a significant improvement in image quality compared to the conventional SART and Algebraic Reconstruction Technique (ART) algorithms.

A Framework for Iterative Reconstruction in Phase-Contrast Computed Tomography Dedicated to the Breast

We present the implementation of a computed tomography (CT) iterative reconstruction strategy developed within the SYRMA-CT project for in vivo phase-contrast CT of uncompressed breast, ongoing at the ELETTRA synchrotron radiation facility (Trieste, Italy). Propagation-based phase-contrast imaging exploits the high spatial coherence of the monoenergetic laminar X-ray beam, as well as the large object-to-detector distance and the use of a Pixirad-8 high-resolution photon-counting CdTe detector. The signal in projection views depends on the X-ray absorption as well as on the phase shift introduced by the breast tissue in the beam path. A phase retrieval algorithm allowed recovering the projected 2-D phase information of the irradiated tissue layer, which was input to the CT reconstruction; then, the breast slices were reconstructed via a simultaneous algebraic reconstruction technique algorithm. Our iterative reconstruction—coupled with a filtering process for reducing the noise level and ring artifacts while preserving edges sharpness—showed better image quality than conventional filtered back projection (FBP) reconstruction. This phantom study showed that our iterative reconstruction produced images with higher contrast-to-noise ratio and spatial resolution than those obtained with FBP. Finally, the developed algorithm removed ring-like artifacts, without worsening the image quality.

Performances of new reconstruction algorithms for CT-TDLAS (computer tomography-tunable diode laser absorption spectroscopy)

Recent advent of the tunable lasers made to measure simultaneous temperature and concentration fields of the gases. CT-TDLAS (computed tomography-tunable diode laser absorption spectroscopy) is one the leading techniques for the measurements of temperature and concentration fields of the gases. In CT-TDLAS, the accuracies of the measurement results are strongly dependent upon the reconstruction algorithms. In this study, four different reconstruction algorithms have been tested numerically using experimental data sets measured by thermocouples for combustion fields. Three reconstruction algorithms, MART (multiplicative algebraic reconstruction technique) algorithm, SART (simultaneous algebraic reconstruction technique) algorithm and SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm, are newly proposed for CT-TDLAS in this study. The calculation results obtained by the three algorithms have been compared with previous algorithm, ART (algebraic reconstruction technique) algorithm. Phantom data sets have been generated by the use of thermocouples data obtained in an actual experiment. The data of the Harvard HITRAN table in which the thermo-dynamical properties and the light spectrum of the H2O are listed were used for the numerical test. The reconstructed temperature and concentration fields were compared with the original HITRAN data, through which the constructed methods are validated. The performances of the four reconstruction algorithms were demonstrated. This method is expected to enhance the practicality of CT-TDLAS.

Acoustic Emission tomography based on simultaneous algebraic reconstruction technique to visualize the damage source location in Q235B steel plate

Acoustic Emission (AE) tomography based on Simultaneous Algebraic Reconstruction Technique (SART), which combines the traditional location algorithm with the SART algorithm by using AE events as its signal sources, is a new visualization method for inspecting and locating the internal damages in the structure. In this paper, the proposed method is applied to examine and visualize two man-made damage source locations in the Q235B steel plate to validate its effectiveness. Firstly, the Q235B steel plate with two holes specimen is fabricated and the pencil lead break (PLB) signal is taken as the exciting source for AE tomography.Secondly, A 6-step description of the SART algorithm is provided and the three dimensional(3D)image contained the damage source locations is visualized by using the proposed algorithm in terms of a locally varying wave velocity distribution. It is shown that the AE tomography based on SART has great potential in the application of structure damage detection. Finally, to further improve the quality of 3D imaging, the Median Filter and the Adaptive Median Filter are used to reduce the noises resulting from AE tomography. The experiment results indicate that Median Filter is the optimal method to remove Salt & Pepper noises.

Constrained SART algorithm for inverse problems in image reconstruction

In this paper we integrate the SART (Simultaneous Algebraic Reconstruction Technique) algorithm into a general iterative method, introduced in [8]. This general method offers us the possibility of achieving a new convergence proof of the SART method and prove the convergence of the constrained version of SART. Systematic numerical experiments, comparing SART and Kaczmarz-like algorithms, are made on two phantoms widely used in image reconstruction literature.