Simultaneous Algebraic Reconstruction Research Articles

Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method.

Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

Evaluation of reconstruction algorithms for a stationary digital breast tomosynthesis system using a carbon nanotube X-ray source array.

Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study.

Noise suppression-guided image filtering for low-SNR CT reconstruction.

In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images. Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm. In each iteration of NSGIFR, the output image of SART reserves more details and is used as input image of GIF, while the image denoised by BM3D serves as guidance image of GIF. Experimental results indicate that the proposed algorithm displays outstanding performance on preserving structures and suppressing noise for low-SNR CT reconstruction. NSGIFR can achieve more superior image quality than SART, POCS-TV and POCS-BM3D in terms of visual effect and quantitative analysis. Graphical abstract Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm.

Low-dose cone-beam CT (LD-CBCT) reconstruction for image-guided radiation therapy (IGRT) by three-dimensional dual-dictionary learning

BackgroundTo develop a low-dose cone beam CT (LD-CBCT) reconstruction method named simultaneous algebraic reconstruction technique and dual-dictionary learning (SART-DDL) joint algorithm for image guided radiation therapy (IGRT) and evaluate its imaging quality and clinical application ability.MethodsIn this retrospective study, 62 CBCT image sets from February 2018 to July 2018 at west china hospital were randomly collected from 42 head and neck patients (mean [standard deviation] age, 49.7 [11.4] years, 12 females and 30 males). All image sets were retrospectively reconstructed by SART-DDL (resultant D-CBCT image sets) with 18% less clinical raw projections. Reconstruction quality was evaluated by quantitative parameters compared with SART and Total Variation minimization (SART-TV) joint reconstruction algorithm with paired t test. Five-grade subjective grading evaluations were done by two oncologists in a blind manner compared with clinically used Feldkamp-Davis-Kress algorithm CBCT images (resultant F-CBCT image sets) and the grading results were compared by paired Wilcoxon rank test. Registration results between D-CBCT and F-CBCT were compared. D-CBCT image geometry fidelity was tested.ResultsThe mean peak signal to noise ratio of D-CBCT was 1.7 dB higher than SART-TV reconstructions (P < .001, SART-DDL vs SART-TV, 36.36 ± 0.55 dB vs 34.68 ± 0.28 dB). All D-CBCT images were recognized as clinically acceptable without significant difference with F-CBCT in subjective grading (P > .05). In clinical registration, the maximum translational and rotational difference was 1.8 mm and 1.7 degree respectively. The horizontal, vertical and sagittal geometry fidelity of D-CBCT were acceptable.ConclusionsThe image quality, geometry fidelity and clinical application ability of D-CBCT are comparable to that of the F-CBCT for head-and-neck patients with 18% less projections by SART-DDL.

Nuclear spin noise tomography in three dimensions with iterative simultaneous algebraic reconstruction technique (SART) processing.

We report three-dimensional spin noise imaging (SNI) of nuclear spin density from spin noise data acquired by Faraday detection. Our approach substantially extends and improves the two-dimensional SNI method for excitation-less magnetic resonance tomography reported earlier (Müller and Jerschow, 2006). This proof of principle was achieved by taking advantage of the particular continuous nature of spin noise acquired in the presence of constant magnitude magnetic field gradients and recent advances in nuclear spin noise spectroscopy acquisition as well as novel processing techniques. In this type of projection-reconstruction-based spin noise imaging the trade-off between signal-to-noise ratio (or image contrast) and resolution can be adjusted a posteriori during processing of the original time-domain data by iterative image reconstruction in a unique way not possible in conventional rf-pulse-dependent magnetic resonance imaging (MRI). The 3D SNI is demonstrated as a proof of concept on a commercial 700 MHz high-resolution NMR spectrometer, using a 3D-printed polymeric phantom immersed in water.

Superiorized method for metal artifact reduction.

Metal artifact reduction (MAR) is a challenging problem in computed tomography (CT) imaging. A popular class of MAR methods replace sinogram measurements that are corrupted by metal with artificial data, typically generated from some combination of interpolation along with other heuristics. While these "projection completion" approaches are successful in eliminating severe artifacts, secondary artifacts may be introduced by the artificial data. In this paper, we propose an approach which uses projection completion to generate a prior image, which is then incorporated into an iterative reconstruction algorithm based on the superiorization framework. The rationale is that the image produced by the iterative algorithm can inherit the desirable properties of the prior image, while also reducing secondary artifacts. The prior image is reconstructed using normalized metal artifact reduction (NMAR), a popular projection completion approach. The iterative algorithm is a modified version of the simultaneous algebraic reconstruction technique (SART), which reduces artifacts by incorporating a polyenergetic forward model, least-squares weighting, and superiorization. The penalty function used for superiorization is a weighted average between a total variation (TV) term and a term promoting similarity with the prior image, similar to penalty functions used in prior image constrained compressive sensing (PICCS). Because the prior is largely free of severe metal artifacts, these artifacts are discouraged from arising during iterative reconstruction; additionally, because the iterative approach uses the original projection data, it is able to recover information that is lost during the NMAR process. We perform numerical experiments modeling a simple geometric object, as well as several more realistic scenarios such as metal pins, bilateral hip implants, and dental fillings placed within an anatomical phantom. The proposed iterative algorithm is largely successful at eliminating severe metal artifacts as well as secondary artifacts introduced by the NMAR process, especially lost edges of bone structures in the neighborhood of the metal regions. In one case modeling severe photon starvation, the NMAR algorithm is found to provide better results. The proposed algorithm is effective in applying the superiorization methodology to the problem of MAR, providing better results than both NMAR and a purely total variation-based superiorization approach in nearly all cases.

Characterizing plasma with emission tomography-Feasibility study on synthetic and experimental data.

We present a feasibility study on different tomographic algorithms to overcome the issues of finite sets of projection data, limited viewing angles, and noisy data, which cause the tomographic reconstruction to be an ill-posed inversion problem. We investigated three approaches: single angle Abel inversion, two angle approach, and multiple angle 2D plasma tomography. These methods were tested on symmetric and asymmetric sample functions and on experimental results from a supersonic flowing argon microwave plasma sustained in a cylindrical quartz cavity. The analysis focused on the afterglow region of the microwave flow where a plasmoid-like formation was observed. We investigated the effects of the uniform random noise added to the simulated data by applying smoothing techniques. The quality of reconstructed images was assessed by using peak signal-to-noise ratio and universal quality image measures. The results showed that the Abel inversion approach could be employed only when the system is radially symmetric, while the systems with slight asymmetry could be reconstructed with the two angle approach. In the complete absence of symmetry, full 2D tomographic reconstruction should be applied. The data analysis showed that the best results were obtained by employing either the filtered back projection or the simultaneous algebraic reconstruction technique. The total variation minimization method proved to be the best denoising technique. Each approach was used to obtain the spatial distributions of argon excited states taken at three positions along the plasmoid-like structure. The results indicated that the plasma was asymmetric with argon populating the cavity surface.

Quantitative three-dimensional nondestructive imaging of whole anaerobic ammonium-oxidizing bacteria.

Anaerobic ammonium-oxidizing (anammox) bacteria play a key role in the global nitrogen cycle and in nitrogenous wastewater treatment. The anammox bacteria ultrastructure is unique and distinctly different from that of other prokaryotic cells. The morphological structure of an organism is related to its function; however, research on the ultrastructure of intact anammox bacteria is lacking. In this study, in situ three-dimensional nondestructive ultrastructure imaging of a whole anammox cell was performed using synchrotron soft X-ray tomography (SXT) and the total variation-based simultaneous algebraic reconstruction technique (TV-SART). Statistical and quantitative analyses of the intact anammox bacteria were performed. High soft X-ray absorption composition inside anammoxosome was detected and verified to be relevant to iron-binding protein. On this basis, the shape adaptation of the anammox bacteria response to iron was explored.

High-quality initial image-guided 4D CBCT reconstruction.

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts because the 4D CBCT reconstruction process is an extreme sparse-view CT procedure wherein only under-sampled projections are used for the reconstruction of each phase. To obtain a set of 4D CBCT images achieving both high spatial and temporal resolution, we propose an algorithm by providing a high-quality initial image at the beginning of the iterative reconstruction process for each phase to guide the final reconstructed result toward its optimal solution. The proposed method consists of three steps to generate the initial image. First, a prior image is obtained by an iterative reconstruction method using the measured projections of the entire set of 4D CBCT images. The prior image clearly shows the appearance of structures in static regions, although it contains blurring artifacts in motion regions. Second, the robust principal component analysis (RPCA) model is adopted to extract the motion components corresponding to each phase-resolved image. Third, a set of initial images are produced by the proposed linear estimation model that combines the prior image and the RPCA-decomposed motion components. The final 4D CBCT images are derived from the simultaneous algebraic reconstruction technique (SART) equipped with the initial images. Qualitative and quantitative evaluations were performed by using two extended cardiac-torso (XCAT) phantoms and two sets of patient data. Several state-of-the-art 4D CBCT algorithms were performed for comparison to validate the performance of the proposed method. The image quality of phase-resolved images is greatly improved by the proposed method in both phantom and patient studies. The results show an outstanding spatial resolution, in which streaking artifacts are suppressed to a large extent, while detailed structures such as tumors and blood vessels are well restored. Meanwhile, the proposed method depicts a high temporal resolution with a distinct respiratory motion change at different phases. For simulation phantom, quantitative evaluations of the simulation data indicate that an average of 36.72% decrease at EI phase and 42% decrease at EE phase in terms of root-mean-square error (RMSE) are achieved by our method when comparing with PICCS algorithm in Phantom 1 and Phantom 2. In addition, the proposed method has the lowest entropy and the highest normalized mutual information compared with the existing methods in simulation experiments, such as PRI, RPCA-4DCT, SMART, and PICCS. And for real patient cases, the proposed method also achieves the lowest entropy value compared with the competitive method. The proposed algorithm can generate an optimal initial image to improve iterative reconstruction performance. The final sequence of phase-resolved volumes guided by the initial image achieves high spatiotemporal resolution by eliminating motion-induced artifacts. This study presents a practical 4D CBCT reconstruction method with leading image quality.

A study on two-dimensional temperature and concentration distribution of Propane-Air premixed flame using CT-TDLAS

To use supplying gases and energy resources efficiently, accurate measurement of irregular gas is necessary. The TDLAS (Tunable laser absorption spectroscopy) technique can be used to control and monitor the supplying gas conditions and combustion of industrial processes. Recently, CT-TDLAS (Computed tomography-tunable diode laser absorption spectroscopy) has been developed to measure the temperature and concentration field of gases. In this study, the 2-dimensional temperature distribution of the Propane-Air premixed flame in several mixing conditions of fuel has been measured by the constructed CT-TDLAS system. 2-Dimensional temperature distributions are measured by 16 path cells. Further, the third-order polynomial regression analysis was applied to resolve the absorption spectra from the incident and transmitted light for a particular gas. The SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm has been adopted for reconstructing the absorption coefficients on the detecting area. As a result of comparing the temperature for the 2-dimensional detecting area using the thermocouple and CT-TDLAS technique, it has been verified that the relative error for the temperatures measured by the thermocouples and calculated by the CT-TDLAS was up to 8%.

Low-Dose X-ray CT Reconstruction Algorithm Using Shearlet Sparse Regularization

Computed tomography (CT) scans produce ionizing radiation in the body, and high-dose CT scans may increase the risk of cancer. Therefore, reducing the CT radiation dose is particularly important in clinical diagnosis, which is achieved mainly by reducing projection views and tube current. However, the projection data are incomplete in the case of sparse views, which may cause stripe artifacts in the image reconstructed by the filtered back projection (FBP) algorithm, thereby losing the details of the image. Low current intensity also increases the noise of the projection data, degrading the quality of the reconstructed image. This study aimed to use the alternating direction method of multipliers (ADMM) to address the shearlet-based sparse regularization problem, which is subsequently referred to as ADMM-shearlet method. The low-dose projection data were simulated by adding Gaussian noise with zero mean to high-dose projection data. Then FBP, simultaneous algebraic reconstruction technique, total variation, and ADMM-shearlet methods were used to reconstruct images. Normalized mean square error, peak signal-to-noise ratio, and universal quality index were used to evaluate the performance of different reconstruction algorithms. Compared with the traditional reconstruction algorithms, the ADMM-shearlet algorithm performed well in suppressing the noise due to the low dose while maintaining the image details.

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.

Computationally Efficient System Matrix Calculation Techniques in Computed Tomography Iterative Reconstruction.

Background:Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line–area integral model (AIM) that highlights the advantages of both LIM and AIMs.Methods:The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow-angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM).Results:The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2.9 times faster. Differences in root mean square error and peak signal-to-noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0.05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0.05), though computations were 4.9 times more intensive than the proposed LIM.Conclusion:We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM–AIM that achieves significantly improved images though with its computational requirements.

Inversion of vertical rainfall field based on earth-space links

The accurate measurement of rain intensity and its distribution in vertical direction can not only help to understand the process of rainfall development, but also play an important role in human life such as agriculture, weather forecasting, water resources management, and natural disaster warning. According to the analysis of the geometric structure of earth-space link and propagation model of electromagnetic wave in atmosphere, in this paper we propose a method to reconstruct two-dimensional(2D) vertical rainfall field by using earth-space links. Firstly, the measured data of micro rain radar (MRR) from Nanjing are used to generate three real vertical rainfall fields which are marked as I, II and III respectively. Secondly, based on the analysis of the earth-space link’s geometry and the effect of signal attenuation from other factors such as scintillation, atmosphere gas and cloud, the vertical rainfall field inversion model is established. According to the power-law relationship between rain intensity and rain attenuation, which is given by International Telecommunication Union (ITU), the simultaneous algebraic reconstruction technique (SART) is used to inverse the vertical rainfall field. Then, one earth station which can receive a 17 GHz signal from satellite is employed to detect the vertical rainfall field. However, the simulation results show that it is difficult for one earth station to achieve the inversion of rainfall field, and that the correlation coefficients between rainfall fields and inversed fields are 0.556, 0.504 and 0.364 respectively. Based on the result, two earth stations are jointly used. In this simulation, the result shows that after 500 iterations the correlation coefficients all increase above 0.98, and the average biases between rainfall field I, II, III and their inversed fields are 0.122, 0.159 and 0.537 mm/h, respectively. Meanwhile, the Euclidean distances decrease to 0.246, 0.235 and 0.812 mm/h, and the relative errors of entropy are both less than 2%. It can be seen from the inversion fields that the vertical distribution of rain rate is close to that of the real field, which suggests that the method proposed in this paper can basically achieve the inversion of vertical rainfall field by using earth-space links. In addition, with the combined detection of three earth stations the accuracy of the inversion results is significantly improved. The correlation coefficients are all close to 1 and the mean deviations are all on the order of 10&lt;sup&gt;–12&lt;/sup&gt; mm/h, indicating that the 2D vertical rainfall fields are accurately reconstructed. In the near future, the satellite constellation system will be globally deployed, which can promote the applications of our method in areas, such as plateaus, mountains and islands, where there exist no traditional observation data, serving as a supplement to existing precipitation measurements.

An effective sinogram inpainting for complementary limited-angle dual-energy computed tomography imaging using generative adversarial networks.

Dual-energy computed tomography (DECT) provides more anatomical and functional information for image diagnosis. Presently, the popular DECT imaging systems need to scan at least full angle (i.e., 360°). In this study, we propose a DECT using complementary limited-angle scan (DECT-CL) technology to reduce the radiation dose and compress the spatial distribution of the imaging system. The dual-energy total scan is 180°, where the low- and high-energy scan range is the first 90° and last 90°, respectively. We describe this dual limited-angle problem as a complementary limited-angle problem, which is challenging to obtain high-quality images using traditional reconstruction algorithms. Furthermore, a complementary-sinogram-inpainting generative adversarial networks (CSI-GAN) with a sinogram loss is proposed to inpainting sinogram to suppress the singularity of truncated sinogram. The sinogram loss focuses on the data distribution of the generated sinogram while approaching the target sinogram. We use the simultaneous algebraic reconstruction technique namely, a total variable (SART-TV) algorithms for image reconstruction. Then, taking reconstructed CT images of pleural and cranial cavity slices as examples, we evaluate the performance of our method and numerically compare different methods based on root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Compared with traditional algorithms, the proposed network shows advantages in numerical terms. Compared with Patch-GAN, the proposed network can also reduce the RMSE of the reconstruction results by an average of 40% and increase the PSNR by an average of 26%. In conclusion, both qualitative and quantitative comparison and analysis demonstrate that our proposed method achieves a good artifact suppression effect and can suitably solve the complementary limited-angle problem.

Self-Guided Limited-Angle Computed Tomography Reconstruction Based on Anisotropic Relative Total Variation

In some computed tomography (CT) applications, limited-angle projections are used for image reconstruction, and traditional reconstruction methods, such as filtered back-projection (FBP) or simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images without prior knowledge assistance. For limited-angle CT reconstruction, total variation minimization (TVM) method is not conductive to recovering image structures. Image reconstruction methods based on anisotropic total variation (ATV) and reweighted anisotropic total variation (RwATV) can significantly reduce the shading artifacts using prior knowledge of the scanning angular range and image sparsity. However, using the knowledge of image sparsity does not further improve image quality in some applications. In this paper, we propose a new reconstruction method based on anisotropic relative total variation (ARTV) for limited-angle CT reconstruction. In ARTV, the windowed inherent variation (WIV) indicates the strength of structure information and WIV values are adaptively determined by local structure information. In limited-angle CT, particular scanning angular range urges us into exerting different strengths on different directions of ARTV. Experiments on FORBILD HEAD phantom, a thoracic image and real projections of a walnut are performed to test the new method. Experimental results show that the destroyed structures are recovered to some extent, and we acquire higher image quality compared to some existing limited-angle CT reconstruction methods.

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.

Comparison and optimization of iterative reconstruction algorithms in digital breast tomosynthesis

Breast cancer has the highest morbidity and mortality among women. Early detection and treatment can effectively improve survival. The digital breast tomosynthesis (DBT) technique uses reconstruction algorithms to get a three-dimensional (3D) image set from a plurality of projection images that are obtained at limited view angles. In this paper, through computer simulation, out-of-focus artifact spread function (ASF), noise spectrum function, and modulation transfer function (MTF) were evaluated to optimize three iterative reconstruction algorithms, including simultaneous algebraic reconstruction technique (SART), maximum-likelihood expectation maximization (MLEM), and ordered-subset MLEM (OS-MLEM). The projection data of phantom were simulated and obtained on Geant4 Application for Tomographic Emission (GATE) platform. Based on the elimination of out-of-focus plane artifacts, the suppression of noise, and the sharpness of the reconstructed objects in frequency domain, our results suggested that the overall best reconstructed image quality was achieved when the iteration numbers were 4, 6, and 3 for SART, MLEM and OS-MLEM, respectively.

Block matching frame based material reconstruction for spectral CT

Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x-ray beam hardening artifacts, we first propose a one-step material reconstruction model based on Taylor’s first-order expansion. Then, we develop a basic material reconstruction method named material simultaneous algebraic reconstruction technique (MSART). Considering the local similarity of each material image, we incorporate a powerful block matching frame (BMF) into the material reconstruction (MR) model and generate a BMF based MR (BMFMR) method. Because the BMFMR model contains the L0-norm problem, we adopt a split-Bregman method for optimization. The numerical simulation and physical phantom experiment results validate the correctness of the material reconstruction algorithms and demonstrate that the BMF regularization outperforms the total variation and no-local mean regularizations.

Measurement Enhancement on Two-Dimensional Temperature Distribution of Methane-Air Premixed Flame Using SMART Algorithm in CT-TDLAS

In this study, the temperature distribution of the Methane-Air premixed flame was measured. In order to enhance the measurement accuracy of the CT-TDLAS (Computed tomography-tunable diode laser absorption spectroscopy), the SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm has been adopted. Further, the SLOS (summation of line of sight) and the CSLOS (corrective summation of line of sight) methods have been adopted to increase measurement accuracies. It has been verified that the relative error for the temperatures measured by the thermocouples and calculated by the CT-TDLAS was about 10%.