Simultaneous Algebraic Reconstruction Research Articles

Three-dimensional flame chemiluminescence tomography reconstruction based on outer contour pre-reconstruction

The computed tomography of chemiluminescence (CTC) can be used to reconstruct a three-dimensional (3D) flame chemiluminescence field to obtain information about the spatial characteristics of the flame. However, additional information is needed to solve the ill-posed inverse problem of the CTC due to the constraints such as economy of CTC system and the number of views. In this study, a PR-SART algorithm is proposed for 3D flame reconstruction by combining the flame outer contour pre-reconstruction model with the simultaneous algebraic reconstruction technique (SART). The influence of the number of pre-reconstruction iterations is analyzed in numerical studies. The reconstruction performance of the SART algorithm is compared with the PR-SART algorithm for two flame structures under various numbers of views and noise conditions. Finally, an OH* chemiluminescence imaging system consisting of 8 ultraviolet (UV) cameras is developed, and evaluated through use of reconstructing the 3D structure of low-swirl flames. Numerical and experimental studies indicate that the proposed algorithm and CTC system are effectively capable of removing the reconstruction error in the flame-free region, improving the reconstruction quality, and reducing the computational cost.

Pig-DTpV: A prior information guided directional TpV algorithm for orthogonal translation computed laminography

The local scanning orthogonal translation computed laminography (OTCL) has great potential for tiny fault detection of laminated structure thin-plate parts. However, it generates limited-angle and truncated projection data, which result in aliasing and truncation artifacts in the reconstructed images. The directional total variation (DTV) algorithm has been demonstrated to achieve highly accurate reconstructed images in limited-angle computed tomography (CT). However, its application in local scanning OTCL has not been explored. Based on this algorithm, we introduce the lp norm to better suppress artifacts, and prior information to further constrain the reconstructed image. Thus, we propose a prior information guided directional total p-variation (DTpV) algorithm (Pig-DTpV). The Pig-DTpV model is a constrained non-convex optimization model. The constraint term are the six DTpV terms, whereas the objective term is the data fidelity term. Then, we use the iterative reweighting strategy and the Chambolle–Pock (CP) algorithm to solve the model. The Pig-DTpV reconstruction algorithm’s performance is compared with other algorithms such as simultaneous algebraic reconstruction technique (SART), TV, reweighted anisotropic-TV (RwATV), and DTV in simulation and real data experiments. The experiment results demonstrate that the Pig-DTpV algorithm can reduce truncation and aliasing artifacts and enhance the quality of reconstructed images.

Phase space reconstruction technique for beam optimization in the Low Energy High Intensity Proton Accelerator (LEHIPA)

The Low Energy High Intensity Proton Accelerator (LEHIPA) has been commissioned to the design energy of 20 MeV at BARC, India. The low energy beam transport (LEBT) channel of LEHIPA consists of two solenoids and drift spaces for matching the 50 keV proton beam from the ECR ion source (ECR-IS) to the RFQ. The ion beam extracted from the ECR-IS also contains molecular species like H2 + and H3 +. Proton fraction in the beam is found to degrade slowly with time due to surface contamination of the plasma chamber and this reduction in proton beam current has implications for longitudinal and transverse beam dynamics. Hence, it becomes important to characterise the beam at different proton fraction levels to understand the end-to-end beam dynamics of LEHIPA. Computed Tomography (CT) technique has been used for the beam phase-space reconstruction in LEHIPA, using a Python program, incorporating the feature of filtering secondary species from the beam profiles measured using slit scanners in the LEBT. Simultaneous Algebraic Reconstruction Technique (SART) is used in the reconstruction since it is identified to be a better technique for a limited number of projections. The reconstruction program is benchmarked with the TraceWin beam dynamics code and implemented on the measured beam profiles to recreate the phase space distribution at the beginning of LEBT. Further, the tomographic reconstruction method is compared with the solenoid scan method and the rms emittance values are found to be in good agreement. The measured tomographic phase space distribution has then been used as TraceWin input for LEHIPA end-end beam dynamics simulations and the LEHIPA beam line parameters are re-optimized for minimum beam emittance growth. This paper presents the simulations of CT technique, benchmarking simulations with TraceWin, phase space reconstruction with measured beam profiles and beam dynamics studies of LEHIPA using the reconstructed beam distributions.

The volumetric ultrasound imaging strategy for quantification of the defects in concrete based on the optimized cumulative kurtosis method

Currently, ultrasonic tomography is widely used for quantifying the status of damage/flaw in various materials including metal, concrete, and composite. It is capable of visualizing the internal damage or flaws by reconstructing the velocity of ultrasound. For most cases, the conventional ultrasonic tomography is adapted for slice-based investigations. Though 3D image can be generated by slice-based method, the involvement of multi-scan interpolation caused high computational cost and unexpected errors. Moreover, for 3D imaging, two essential factors are dominant: (i) reconstructing the velocity; and (ii) scheme of forming up the 3D image. To this end, this study proposed to develop a novel volumetric ultrasound imaging strategy with high accuracy. To obtain more accurate Time-of-Flight (TOF) for ultrasonic velocity, an improved method based on cumulative kurtosis is proposed. With the proposed method, the adverse influence of internal complexity within samples on ultrasound was reduced. The proposed novel volumetric ultrasound imaging strategy was verified numerically and experimentally. Finally, the performance of the proposed method has been evaluated by the comparison of three-dimensional imaging results with different inclusion. Additionally, the parametric study was conducted using path average velocity, voxel velocity, and image accuracy. The results show a positive correlation between the number of voxels and imaging accuracy. However, as the number of voxels increases, the errors introduced by the Simultaneous Algebraic Reconstruction Technique (SART) increase. The influential factors on the imaging accuracy were discussed, such as inclusion eccentricity, the relationship among the reduced volume imaging quantity, and accuracy in voxel inversion result. For further application, the recommendations were also provided.

Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction.

Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, ℓ0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP). To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image. This study presents a multiobjective optimization model incorporates both data fidelity term and ℓ0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective function.The iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use ℓ0 minimization to optimize ℓ0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step. With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods. The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge detail restoration.

Sparse-View Neutron Computed Tomography 3-D Reconstruction via the Fast Gradient Projection Algorithm

Neutron tomography is an efficient nondestructive testing technique. As a complement to X-ray computed tomography, it has been widely used in various fields. Due to the difficulty of obtaining complete neutron projection data in a high-radiation environment and the high noise characteristics of neutron images, it is difficult to reconstruct a high-quality image using the conventional filtered-back projection (FBP) algorithm. Therefore, research on sparse-view reconstruction algorithms in neutron tomography is needed. To improve the quality of neutron three-dimensional reconstructed images, this paper proposes an algorithm that combines the Simultaneous Algebraic Reconstruction Technique (SART) with Fast Gradient Projection (FGP), where the FGP is an algorithm for image denoising and deblurring based on the discrete total variation (TV) minimization model. The algorithm proposed in this paper is compared with other algorithms (FBP, SART, and SART-TV) by simulated experimental data and real neutron experimental data. The experimental results show that the novel algorithm outperforms the other three algorithms in terms of denoising and retaining detailed structural information.

Motion compensated cone-beam CT reconstruction using an a priori motion model from CT simulation: a pilot study

Objective. To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) was previously developed. MC-SART employs a 4D-CBCT reconstruction to obtain an initial model, which suffers from a lack of sufficient projections in each bin. The purpose of this study is to demonstrate the feasibility of introducing a motion model acquired during CT simulation to MC-SART, coined model-based CBCT (MB-CBCT). Approach. For each of 5 patients, we acquired 5DCTs during simulation and pre-treatment CBCTs with a simultaneous breathing surrogate. We cross-calibrated the 5DCT and CBCT breathing waveforms by matching the diaphragms and employed the 5DCT motion model parameters for MC-SART. We introduced the Amplitude Reassignment Motion Modeling technique, which measures the ability of the model to control diaphragm sharpness by reassigning projection amplitudes with varying resolution. We evaluated the sharpness of tumors and compared them between MB-CBCT and 4D-CBCT. We quantified sharpness by fitting an error function across anatomical boundaries. Furthermore, we compared our MB-CBCT approach to the traditional MC-SART approach. We evaluated MB-CBCT’s robustness over time by reconstructing multiple fractions for each patient and measuring consistency in tumor centroid locations between 4D-CBCT and MB-CBCT. Main results. We found that the diaphragm sharpness rose consistently with increasing amplitude resolution for 4/5 patients. We observed consistently high image quality across multiple fractions, and observed stable tumor centroids with an average 0.74 ± 0.31 mm difference between the 4D-CBCT and MB-CBCT. Overall, vast improvements over 3D-CBCT and 4D-CBCT were demonstrated by our MB-CBCT technique in terms of both diaphragm sharpness and overall image quality. Significance. This work is an important extension of the MC-SART technique. We demonstrated the ability of a priori 5DCT models to provide motion compensation for CBCT reconstruction. We showed improvements in image quality over both 4D-CBCT and the traditional MC-SART approach.

Vertical Distribution Mapping for Methane Fugitive Emissions Using Laser Path-Integral Sensing in Non-Cooperative Open Paths.

Observing the vertical diffusion distribution of methane fugitive emissions from oil/gas facilities is significant for predicting the pollutant's spatiotemporal transport and quantifying the random emission sources. A method is proposed for methane's vertical distribution mapping by combining the laser path-integral sensing in non-non-cooperative open paths and the computer-assisted tomography (CAT) techniques. It uses a vertical-plume-mapping optical path configuration and adapts the developed dynamic relaxation and simultaneous algebraic reconstruction technique (DR-SART) into methane-emission-distribution reconstruction. A self-made miniaturized TDLAS telemetry sensor provides a reliable path to integral concentration information in non-non-cooperative open paths, with Allan variance analysis yielding a 3.59 ppm·m sensitivity. We employed a six-indexes system for the reconstruction performance analysis of four potential optical path-projection configurations and conducted the corresponding validation experiment. The results have shown that that of multiple fan-beams combined with parallel-beam modes (MFPM) is better than the other optical path-projection configurations, and its reconstruction similarity coefficient (ε) is at least 22.4% higher. For the different methane gas bag-layout schemes, the reconstruction errors of maximum concentration (γm) are consistently around 0.05, with the positional errors of maximum concentration (δ) falling within the range of 0.01 to 0.025. Moreover, considering the trade-off between scanning duration and reconstruction accuracy, it is recommended to appropriately extend the sensor measurement time on a single optical path to mitigate the impact of mechanical vibrations induced by scanning motion.

Estimation of pollutant source using source-receptor response matrix from maximum-length sequence pulse technique

Accurate estimation of pollutant sources in built environments is crucial for monitoring air quality and preventing harm to health and property. This study presented an improved method for estimating the dynamical pollutant source strength using the concentration signal monitored at the receptor point. We employed a built environment system subjected to a pollutant pulse signal in accordance with the maximum-length sequence (m-sequence) to acquire response concentration. Subsequently, the source-receptor response matrix was inversely identified based on the known pulse signal and the response concentration. Then, the unknown source release rate was inversely estimated by the monitored concentration signal and previously identified response matrix. In addition, the performances of different inverse algorithms and m-sequence orders were also analyzed. Here both simulation and application to a real experiment of a wind duct were implemented, and the results demonstrated the higher-order m-sequence method exhibited better noise resistance compared to the traditional unit pulse method. In both simulation and experiment, the m-sequence pulse method reduced the root mean square error (RMSE) by 39.9 % and 70.7 %, respectively, compared to the unit pulse method. In experiment, the simultaneous multiplicative algebraic reconstruction technique (SMART) combined with the Tikhonov regularization (TR) method showed relatively stable performance.

Dynamic imaging of three-dimensional temperature field via three-directional wavelength modulated lateral shearing interferometry

A three-directional wavelength modulated lateral shearing interference system was proposed for the dynamic three-dimensional visualization of a temperature field. The laser emitted from a distributed feedback (DFB) laser was divided into three beams by a fiber beam splitter. The beams were spatially expanded and passed through the flame in three directions of 0°, 120° and 240°, respectively. The three interferograms formed by reflections from the front and back surfaces of the parallel glass plates were spatially converged into the lens of a high-speed camera. Multiple plane mirrors were utilized to reflect the beams. The size of the region of interest (ROI) was −20 ∼ 20 mm in both x and z directions and 5 ∼ 30 mm in y direction, with a spatial resolution of 200 μm in all directions. A three-dimensional sensitivity matrix was modeled, and the simultaneous algebraic reconstruction technique (SART) was used for layer-by-layer imaging to achieve three-dimensional flame monitoring. Numerical simulations verified the effectiveness of the algorithm qualitatively and quantitatively. In experiments, the temperature distributions of four cases of flames, namely, steady diffused flame, partially blocked diffused flame, dynamic acoustically excited flame and the flame after ignition, were reconstructed at 1k frames per second (fps). The flame structures and temporal variations of temperature distributions were clearly visualized, demonstrating the ability of the system in fast monitoring of three-dimensional dynamic temperature fields.

Study on the Location Determination of Building Fire Points Based on Acoustic CT Temperature Measurement

Rapid perception of the location of the fire point is crucial to building fire emergency response in the process of building fire emergency response, which can help firefighters direct fire-fighting operations, effectively control fire sources, and provide strong evidence for the analysis and investigation of fire causes. This paper uses acoustic CT temperature measurement technology to determine the fire source location of a building fire and verifies its validity and applicability as follows: we construct various fire point numerical models based on the fire dynamics simulator (FDS) and obtain temperature data at different times; neural network means were used to obtain the time-of-flight (TOF) of an acoustic wave traveling; the large ill-conditioned matrix equation of acoustic flight under different meshing schemes was constructed and solved based on the simultaneous algebraic reconstruction technique (SART) and least squares QR-decomposition (LSQR), and then reconstruction temperature data under each scheme were obtained. Through the error analysis, the reconstruction effect of each reconstruction scheme is evaluated, and then the applicability of the location coordinate determination of the fire point is analyzed. The results show that the determination of the fire location under the conditions of various fire points in the building space can be realized by acoustic CT temperature measurement technology.

Joint Bi-Modal Tomographic Reconstruction from Observations with Missing Data

A method for tomographic reconstruction of bi-modal data sets using cross modal regularization was developed. The method constrains the Simultaneous Algebraic Reconstruction Technique by using clustering-based segmented images and quality controlled fused images, to jointly regularize the reconstruction of structures imaged with different methods. The joint regularized reconstruction method was tested using a synthetic X-Ray and neutron data set with a mix of attenuation driven data starvation. Assuming consistent material boundaries in the different imaging modalities, it was shown that improvements in terms of the Feature Similarity Index Measure are made compared to a traditional SART-TV reconstruction. UK Ministry of Defence ©Crown Owned Copyright 2023/AWE

Improved sparse domain super-resolution reconstruction algorithm based on CMUT.

A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects.

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>

An Extended Simultaneous Algebraic Reconstruction Technique for Imaging the Ionosphere Using GNSS Data and Its Preliminary Results

To generate high-quality reconstructions of ionospheric electron density (IED), we propose an extended simultaneous algebraic reconstruction technique (ESART). The ESART method distributes the discrepancy between the actual GNSS TEC and the calculated TEC among the ray–voxels based on the contribution of voxels to GNSS TEC, rather than the ratio of the length of ray–voxel intersection to the sum of the lengths of all ray–voxel intersections, as is adopted by conventional methods. The feasibility of the ESART method for reconstructing the IED under different levels of geomagnetic activities is addressed. Additionally, a preliminary experiment is performed using the reconstructed IED profiles and comparing them with ionosonde measurements, which provide direct observations of electron density. The root mean square errors (RMSE) and absolute errors of the ESART method, the simultaneous algebraic reconstruction technique (SART) method, and the International Reference Ionosphere (IRI) 2016 model are calculated to evaluate the effectiveness of the proposed method. Compared to the conventional SART method of ionospheric tomography and the IRI-2016 model, the reconstructed IED profiles obtained using the ESART method are in better agreement with the electron density obtained from the ionosondes, especially for the peak electron densities (NmF2). In addition, a case study of an intense geomagnetic storm on 17–19 March 2015 shows that the spatial and temporal features of storm-related ionospheric disturbances can be more clearly depicted using the ESART method than with the SART method.

Fast and accurate flow measurement through dual-camera light field particle image velocimetry and ordered-subset algorithm

Light field particle image velocimetry (LF-PIV) can measure the three-dimensional (3D) flow field via a single perspective and hence is very attractive for applications with limited optical access. However, the flow velocity measurement via single-camera LF-PIV shows poor accuracy in the depth direction due to the particle reconstruction elongation effect. This study proposes a solution based on a dual-camera LF-PIV system along with an ordered-subset simultaneous algebraic reconstruction technique (OS-SART). The proposed system improves the spatial resolution in the depth direction and reduces the reconstruction elongation. The OS-SART also reduces the computational time brought by the dual-camera LF-PIV. Numerical reconstructions of the particle fields and Gaussian ring vortex field are first performed to evaluate the reconstruction accuracy and efficiency of the proposed system. Experiments on a circular jet flow are conducted to further validate the velocity measurement accuracy. Results indicate that the particle reconstruction elongation is reduced more than 10 times compared to the single-camera LF-PIV and the reconstruction efficiency is improved at least twice compared to the conventional SART. The accuracy is improved significantly for the ring vortex and 3D jet flow fields compared to the single-camera system. It is therefore demonstrated that the proposed system is capable of measuring the 3D flow field fast and accurately.

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm3 IC (≃6 × 108 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

Dynamic Speed of Sound Adaptive Transmission-Reflection Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) can visualize a target with multiple imaging contrasts, which were demonstrated individually previously. Here, to improve the imaging quality, the dynamic speed of sound (SoS) map derived from the transmission USCT will be adapted for the correction of the acoustic speed variation in the reflection USCT. The variable SoS map was firstly restored via the optimized simultaneous algebraic reconstruction technique with the time of flights selected from the transmitted ultrasonic signals. Then, the multi-stencils fast marching method was used to calculate the delay time from each element to the grids in the imaging field of view. Finally, the delay time in conventional constant-speed-assumed delay and sum (DAS) beamforming would be replaced by the practical computed delay time to achieve higher delay accuracy in the reflection USCT. The results from the numerical, phantom, and in vivo experiments show that our approach enables multi-modality imaging, accurate target localization, and precise boundary detection with the full-view fast imaging performance. The proposed method and its implementation are of great value for accurate, fast, and multi-modality USCT imaging, particularly suitable for highly acoustic heterogeneous medium.

Performance improvement for additive light field displays with weighted simultaneous algebra reconstruction technique and tracked views

AbstractAdditive light field displays are transparent autostereoscopic three‐dimensional displays without backlights, thus suitable for augmented reality applications. However, when the parallax between viewpoint images becomes large with the increase of viewing angle, the optimization algorithm is hard to handle too many dissimilarities evenly on all viewpoints, resulting in poor reconstructed quality. This paper presents an additive light field display using the weighted simultaneous algebraic reconstruction technique with viewing angle‐dependent weight distribution functions. We constrain the optimization to deliver a reconstructed light field of high image quality for viewpoints of large weight. When the proposed method is applied, with a wide dynamic viewing angle of 57° × 43°, the tracked views' peak signal‐to‐noise ratio exceeds 30 dB with only two additive display layers.

Spectral CT reconstruction via low-rank representation and structure preserving regularization

Objective: With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cut into several narrow bins which leads to the result that only a part of photon can be collected in each individual energy channel.This can severely degrade the image qualities. To address this problem, we propose a spectral CT reconstruction algorithm based on low-rank representation and structure preserving regularization in this paper. Approach: To make full use of the prior knowledge about both the inter-channel correlation and the sparsity in gradient domain of inner-channel data, this paper combines a low-rank correlation descriptor with a structure extraction operator as priori regularization terms for spectral CT reconstruction. Furthermore, a split-Bregman based iterative algorithm is developed to solve the reconstruction model. Finally, we propose a multi-channel adaptive parameters generation strategy according to CT values of each individual energy channel. Main results: Experimental results on numerical simulations and real mouse data indicate that the proposed algorithm achieves higher accuracy on both reconstruction and material decomposition than the methods based on simultaneous algebraic reconstruction technique (SART), total variation minimization (TVM), total variation with low-rank (LRTV), and spatial-spectral cube matching frame (SSCMF). Compared with SART, our algorithm improves the feature similarity (FSIM) by 40.4% on average for numerical simulation reconstruction, whereas TVM, LRTV, and SSCMF correspond to 26.1%, 28.2%, and 29.5%, respectively. Significance: We outline a multi-channel reconstruction algorithm tailored for spectral CT. The qualitative and quantitative comparisons present a significant improvement of image quality, indicating its promising potential in spectral CT imaging.