Low-dose CT coronary angiography using iterative reconstruction with a 256-slice CT scanner

AIM:To explore whether computer tomography coronary angiography(CTCA) using iterative reconstruction(IR) leads to significant radiation dose reduction without a significant loss in image interpretability compared to conventional filtered back projection(FBP).METHODS:A consecutive series of 200 patients referred to our institution to undergo CTCA constituted the study population.Patients were sequentially assigned to FBP or IR.All studies were acquired with a 256-slice CT scanner.A coronary segment was considered interpretable if image quality was adequate for evaluation of coronary lesions in all segments ≥ 1.5 mm.RESULTS:The mean age was 56.3±9.6 years and165(83%) were male,with no significant differences between groups.Most scans were acquired using prospective ECG triggering,without differences between groups(FBP 84%vs IR 82%;P=0.71).A total of 3198(94%) coronary segments were deemed of diagnostic quality.The percent assessable coronary segments was similar between groups(FBP 91.7%±4.0% vs IR92.5% ± 2.8%; P=0.12).Radiation dose was significantly lower in the IR group(2.8±1.4 mSvvs 4.6±3.0mSv;P<0.0001).Image noise(37.8±1.4 HUvs 38.2±2.4 HU; P=0.20) and signal density(461.7±51.9HU vs 462.2±51.2 HU; P=0.54) levels did not differ between FBP and IR groups,respectively.The IR group was associated to significant effective dose reductions,irrespective of the acquisition mode.CONCLUSION:Application of IR in CTCA preserves image interpretability despite a significant reduction in radiation dose.

Poloidal magnetic field reconstruction by laser-driven ion-beam trace probe in spherical tokamak

The poloidal magnetic field( B_(p)) plays a critical role in plasma equilibrium, confinement and transport of magnetic confinement devices. Multiple diagnostic methods are needed to complement each other to obtain a more accurate B_(p) profile. Recently, the laser-driven ion-beam trace probe(LITP) has been proposed as a promising tool for diagnosing B_(p) and radial electric field( E_(r)) profiles in tokamaks [Yang X Y et al 2014 Rev. Sci. Instrum. 85 11E429]. The spherical tokamak(ST) is a promising compact device with high plasma beta and naturally large elongation. However, when applying LITP to diagnosing B_(p) in STs, the larger B_(p) invalidates the linear reconstruction relationship for conventional tokamaks, necessitating the development of a nonlinear reconstruction principle tailored to STs. This novel approach employs an iterative reconstruction method based on Newton's method to solve the nonlinear equation. Subsequently,a simulation model to reconstruct the B_(p) profile of STs is developed and the experimental setup of LITP is designed for EXL-50, a middle-sized ST. Simulation results of the reconstruction show that the relative errors of B_(p) reconstruction are mostly below 5%. Moreover, even with 5 mm measurement error on beam traces or 1 cm flux surface shape error, the average relative error of reconstruction remains below 15%, initially demonstrating the robustness of LITP in diagnosing B_(p) profiles in STs.

Non-enhanced Low-tube-voltage High-pitch Dual-source Computed Tomography with Sinogram Affirmed Iterative Reconstruction Algorithm of the Abdomen and Pelvis

Objective To investigate the image quality, radiation dose and diagnostic value of the low-tube-voltage high-pitch dual-source computed tomography(DSCT) with sinogram affirmed iterative reconstruction(SAFIRE) for non-enhanced abdominal and pelvic scans. Methods This institutional review board-approved prospective study included 64 patients who gave written informed consent for additional abdominal and pelvic scan with DSCT in the period from November to December 2012. The patients underwent standard non-enhanced CT scans(protocol 1) [tube voltage of 120 k Vp/pitch of 0.9/filtered back-projection(FBP) reconstruction] followed by high-pitch non-enhanced CT scans(protocol 2)(100 k Vp/3.0/SAFIRE). The total scan time, mean CT number, signal-to-noise ratio(SNR), image quality, lesion detectability and radiation dose were compared between the two protocols. Results The total scan time of protocol 2 was significantly shorter than that of protocol 1(1.4±0.1 seconds vs. 7.6±0.6 seconds, P<0.001). There was no significant difference between protocol 1 and protocol 2 in mean CT number of all organs(liver, 55.4±6.3 HU vs. 56.1±6.8 HU, P=0.214; pancreas, 43.6±5.9 HU vs. 43.7±5.8 HU, P=0.785; spleen, 47.9±3.9 HU vs. 49.4±4.3 HU, P=0.128; kidney, 32.2±2.3 HU vs. 33.1±2.3 HU, P=0.367; abdominal aorta, 44.8±5.6 HU vs. 45.0±5.5 HU, P=0.499; psoas muscle, 50.7±4.1 HU vs. 50.3±4.5 HU, P=0.279). SNR on images of protocol 2 was higher than that of protocol 1(liver, 5.0±1.2 vs. 4.5±1.1, P<0.001; pancreas, 4.0±1.0 vs. 3.6±0.8, P<0.001; spleen, 4.7±1.0 vs. 4.1±0.9, P<0.001; kidney, 3.1±0.6 vs. 2.8±0.6, P<0.001; abdominal aorta, 4.1±1.0 vs. 3.8±1.0, P<0.001; psoas muscle, 4.5±1.1 vs. 4.3±1.2, P=0.012). The overall image noise of protocol 2 was lower than that of protocol1(9.8±3.1 HU vs. 11.1±3.0 HU, P<0.001). Image quality of protocol 2 was good but lower than that of protocol 1(4.1±0.7 vs. 4.6±0.5, P<0.001). Protocol 2 perceived 229 of 234 lesions(97.9%) that were detected in protocol 1 in the abdomen and pelvis. Radiation dose of protocol 2 was lower than that of protocol 1(4.4±0.4 m Sv vs. 7.3±2.4 m Sv, P<0.001) and the mean dose reduction was 41.4%. Conclusion The high-pitch DSCT with SAFIRE can shorten scan time and reduce radiation dose while preserving image quality in non-enhanced abdominal and pelvic scans.

Value of liver computed tomography with iodixanol 270, 80 kVp and iterative reconstruction

AIM: To evaluate the image quality of hepatic multidetector computed tomography(MDCT) with dynamic contrast enhancement. METHODS: It uses iodixanol 270 mg/m L(Visipaque 270) and 80 kVp acquisitions reconstructed with sinogram affirmed iterative reconstruction(SAFIRE?) in comparison with a standard MDCT protocol. Fiftythree consecutive patients with known or suspected hepatocellular carcinoma underwent 55 CT examinations, with two different four-phase CT protocols. The first group of 30 patients underwent a standard 120 kVp acquisition after injection of Iohexol 350 mg/m L(Accupaque 350~?) and reconstructed with filtered back projection. The second group of 25 patients underwent a dual-energy CT at 80-140 kVp with iodixanol 270. The 80 kVp component of the second group was reconstructed iteratively(SAFIRE?-Siemens). All hyperdense and hypodense hepatic lesions ≥ 5 mm were identified with both protocols. Aorta and portal vessels/liver parenchyma contrast to noise ratio(CNR) in arterial phase, hypervascular lesion/liver parenchyma CNR in arterial phase, hypodense lesion/liver parenchyma CNR in portal and late phase were calculated in both groups.RESULTS: Aorta/liver and focal lesions altogether/liver CNR were higher for the second protocol(P = 0.0078 and 0.0346). Hypervascular lesions/liver CNR was not statistically different(P = 0.86). Hypodense lesion/liver CNR in the portal phase was significantly higher for the second group(P = 0.0107). Hypodense lesion/liver CNR in the late phase was the same for both groups(P = 0.9926).CONCLUSION: MDCT imaging with 80 kVp with iterative reconstruction and iodixanol 270 yields equal or even better image quality.

Novel Fourier-based iterative reconstruction for sparse fan projection using alternating direction total variation minimization

Sparse-view x-ray computed tomography （CT） imaging is an interesting topic in CT field and can efficiently decrease radiation dose. Compared with spatial reconstruction, a Fourier-based algorithm has advantages in reconstruction speed and memory usage. A novel Fourier-based iterative reconstruction technique that utilizes non-uniform fast Fourier transform （NUFFF） is presented in this work along with advanced total variation （TV） regularization for a fan sparse-view CT. The proposition of a selective matrix contributes to improve reconstruction quality. The new method employs the NUFFT and its adjoin to iterate back and forth between the Fourier and image space. The performance of the proposed algorithm is demonstrated through a series of digital simulations and experimental phantom studies. Results of the proposed algorithm are compared with those of existing TV-regularized techniques based on compressed sensing method, as well as basic algebraic reconstruction technique. Compared with the existing TV-regularized techniques, the proposed Fourier-based technique significantly improves convergence rate and reduces memory allocation, respectively.

High-Quality Three-Dimensional Computed Tomography Angiography of Abdominal Viscera with Small Focal Spot, Low Tube Voltage, and Iterative Model Reconstruction Technique

Purpose: To evaluate the quality of three-dimensional (3D) CT angiography images of the abdominal viscera with small focal spot, low tube voltage, and iterative model reconstruction technique (IMR). Materials and Methods: Seven patients with suspected disease of the pancreatobiliary system had undergone CT with high-quality CTA protocol in the present study. There were 5 men and 2 women, ranging in age from 52 to 80 years (mean: 64 years). Results: Depiction of abdominal small artery, small portal vein was possible in all cases. In two cases that we were able to compare, it was superior to standard CTA in small vascular depiction in CTA made clearly in high quality protocol. Conclusions: Although the use of small focal spot, low tube voltage, and IMR can produce higher-quality images of abdominal vessels than standard CTA, this improvement is not significant at elevated radiation doses.

Preliminary clinical application of an adaptive iterative statistical reconstruction algorithm inhead and neck computed tomography angiography with low tube voltage and a low concentration of contrast medium

Objective To evaluate the feasibility of using a low concentration of contrast medium （Visipaque 270 mgl/mL）, low tube voltage, and an advanced image reconstruction algorithm in head and neck computed tomography angiography （CTA）. Methods Forty patients （22 men and 18 women; average age 48.7 ± 14.25 years; average body mass index 23.9 ± 3.7 kg/m^2） undergoing CTA for suspected vascular diseases were randomly assigned into two groups. Group A （n = 20） was administered 370 mgl/mL contrast medium, and group B （n = 20） was administered 270 mgl/mL contrast medium. Both groups were administered at a rate of 4.8 mL/s and an injection volume of 0.8 mL/kg. Images of group A were obtained with 120 kVp and filtered back projection （FBP） reconstruction, whereas images of group B were obtained with 80 kVp and 80% adaptive iterative statistical reconstruction algorithm （ASiR）. The CT values and standard deviations of intracranial arteries and image noise on the corona radiata were measured to calculate the contrast-to-noise ratio （CNR） and signal-to-noise ratio （SNR）. The beam-hardening artifacts （BHAs） around the skull base were calculated. Two readers evaluated the image quality with volume rendered images using scores from 1 to 5. The values between the two groups were statistically compared. Results The mean CT value of the intracranial arteries in group B was significantly higher than that in group A （P 〈 0.001）. The CNR and SNR values in group B were also statistically higher than those in group A （P 〈 0.001）. Image noise and BHAs were not significantly different between the two groups. The image quality score of VR images of in group B was significantly higher than that in group A （P = 0.001）. However, the quality scores of axial enhancement images in group B became significantly smaller than those in group A （P〈 0.001）. The CT dose index volume and dose-length product were decreased by 63.8% and 64%, respectively, in group B （P 〈 0.001 for both）. Conclusion Visipaque combined with 80 kVp and 80% ASiR provided similar image quality in intracranial CTA with 64% radiation dose reduction compared with the use of lopamidol, 120 kVp, and FBP reconstruc-tion.

Fast parallel algorithm for three-dimensional distance-driven model in iterative computed tomography reconstruction

The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model（DDM） is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU（graphic processing unit） platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.

Electrical Impedance Tomography Image Reconstruction Using Iterative Lavrentiev and L-Curve-Based Regularization Algorithm

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution inside a medium from measurements made on its surface. The impedance distribution reconstruction in EIT is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods are often used in solving inverse problems. However, for EIT image reconstruction, the generalized Tikhonov regularization methods may lose the boundary information due to its smoothing operation. In this paper, we propose an iterative Lavrentiev regularization and L-curve-based algorithm to reconstruct EIT images. The regularization parameter should be carefully chosen, but it is often heuristically selected in the conventional regularization-based reconstruction algorithms. So, an L-curve-based optimization algorithm is used for selecting the Lavrentiev regularization parameter. Numerical analysis and simulation results are performed to illustrate EIT image reconstruction. It is shown that choosing the appropriate regularization parameter plays an important role in reconstructing EIT images.

Passive seismic velocity tomography on longwall mining panel based on simultaneous iterative reconstructive technique (SIRT)

Mining operation, especially underground coal mining, always has the remarkable risks of ground control. Passive seismic velocity tomography based on simultaneous iterative reconstructive technique （SIRT） inversion is used to deduce the stress redistribution around the longwall mining panel. The mining-induced microseismic events were recorded by mounting an array of receivers on the surface, above the active panel. After processing and filtering the seismic data, the three-dimensional tomography images of the p-wave velocity variations by SIRT passive seismic velocity tomography were provided. To display the velocity changes on coal seam level and subsequently to infer the stress redistribution, these three-dimensional tomograms into the coal seam level were sliced. In addition, the boundary element method （BEM） was used to simulate the stress redistribution. The results show that the inferred stresses from the passive seismic tomograms are conformed to numerical models and theoretical concept of the stress redistribution around the longwall panel. In velocity tomograms, the main zones of the stress redistribution arotmd the panel, including front and side abutment pressures, and gob stress are obvious and also the movement of stress zones along the face advancement is evident. Moreover, the effect of the advance rate of the face on the stress redistribution is demonstrated in tomography images. The research result proves that the SIRT passive seismic velocity tomography has an ultimate potential for monitoring the changes of stress redistribution around the longwall mining panel continuously and subsequently to improve safety of mining operations.

Application of a simultaneous iterations reconstruction technique for a 3-D water vapor tomography system

The simultaneous iterations rithms of the ART family. It is used reconstruction technique （SIRT） widely in tomography because of is one of several reconstruction algoits convenience in dealing with large sparse matrices. Its theoretical background and iteration model are discussed at the beginning of this paper. Then, the implementation of the SIRT to reconstruct the three-dimensional distribution of water vapor by simulation is discussed. The results show that the SIRT can function effectively in water vapor tomography, obtain rapid convergence, and be implemented more easily than inversion.

Geophysical data sparse reconstruction based on L0-norm minimization

Missing data are a problem in geophysical surveys, and interpolation and reconstruction of missing data is part of the data processing and interpretation. Based on the sparseness of the geophysical data or the transform domain, we can improve the accuracy and stability of the reconstruction by transforming it to a sparse optimization problem. In this paper, we propose a mathematical model for the sparse reconstruction of data based on the LO-norm minimization. Furthermore, we discuss two types of the approximation algorithm for the LO- norm minimization according to the size and characteristics of the geophysical data： namely, the iteratively reweighted least-squares algorithm and the fast iterative hard thresholding algorithm. Theoretical and numerical analysis showed that applying the iteratively reweighted least-squares algorithm to the reconstruction of potential field data exploits its fast convergence rate, short calculation time, and high precision, whereas the fast iterative hard thresholding algorithm is more suitable for processing seismic data, moreover, its computational efficiency is better than that of the traditional iterative hard thresholding algorithm.

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure(DOD), Direction of Arrival(DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the TemporalSpatial Nested Sampling Model(TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom(DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window(TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the highdimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound(CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.

Image reconstruction from few views by l_0-norm optimization

In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory. To overcome the TV model＇s disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the l0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the l0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding （IHT） algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.

Randomized Kaczmarz algorithm for CT reconstruction

The order of the projection in the algebraic reconstruction technique(ART)method has great influence on the rate of the convergence.Although many scholars have studied the order of the projection,few theoretical proofs are given.Thomas Strohmer and Roman Vershynin introduced a randomized version of the Kaczmarz method for consistent,and over-determined linear systems and proved whose rate does not depend on the number of equations in the systems in 2009.In this paper,we apply this method to computed tomography(CT)image reconstruction and compared images generated by the sequential Kaczmarz method and the randomized Kaczmarz method.Experiments demonstrates the feasibility of the randomized Kaczmarz algorithm in CT image reconstruction and its exponential curve convergence.

A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Metal objects in X-ray computed tomography can cause severe artifacts.The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods.This paper proposes a projectiondomain algorithm to reduce the metal artifacts.In this algorithm,the unknowns are the metal-affected projections,while the objective function is set up in the image domain.The data fidelity term is not utilized in the objective function.The objective function of the proposed algorithm consists of two terms:the total variation of the metalremoved image and the energy of the negative-valued pixels in the image.After the metal-affected projections are modified,the final image is reconstructed via the filtered backprojection algorithm.The feasibility of the proposed algorithm has been verified by real experimental data.

Radar Imaging Based on Iterative Algorithms

It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.

Geometrically Nonlinear Deformation Reconstruction Based on iQS4 Elements Using a Linearized Iterative iFEM Algorithm

Structural shape monitoring plays a vital role in the structural health monitoring systems.The inverse finite element method(iFEM)has been demonstrated to be a practical method of deformation reconstruction owing to its unique advantages.Current iFEM formulations have been applied to small deformation of structures based on the small-displacement assumption of linear theory.However,this assumption may be inapplicable to some structures with large displacements in practical applications.Therefore,geometric nonlinearity needs to be considered.In this study,to expand the practical utility of iFEM for large displacement monitoring,we propose a nonlinear iFEM algorithm based on a four-node inverse quadrilateral shell element iQS4.Taking the advantage of an iterative iFEM algorithm,a nonlinear response is linearized to compute the geometrically nonlinear deformation reconstruction,like the basic concept of nonlinear FE analysis.Several examples are solved to verify the proposed approach.It is demonstrated that large displacements can be accurately estimated even if the in-situ sensor data includes different levels of randomly generated noise.It is proven that the nonlinear iFEM algorithm provides a more accurate displacement response as compared to the linear iFEM methodology for structures undergoing large displacement.Hence,the proposed approach can be utilized as a viable tool to effectively characterize geometrically nonlinear deformations of structures in real-time applications.

A WEIGHTED ITERATIVE METHOD FOR ROBUST SELF-CALIBRATION

A robust self-calibration method is presented, which can efficiently discard the outliers based on a Weighted Iteration Method (WIM). The method is an iterative process in which the projective reconstruction is obtained based on the weights of all the points, whereas the weights are defined in inverse proportion to the re- ciprocal of the re-projective errors. The weights of outliers trend to zero after several iterations, and the accu- rate projective reconstruction is determined. The location of the absolute conic and the camera intrinsic pa- rameters are obtained after the projective reconstruction. The theory and experiments with both simulate and real data demonstrate that the proposed method is very efficient and robust.