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 i...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.展开更多
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 ang...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.展开更多
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 iterat...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.展开更多
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 reconstr...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.展开更多
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 proof...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.展开更多
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 proje...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.展开更多
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 suc...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.展开更多
If a spatial-domain function has a finite support,its Fourier transform is an entire function.The Taylor series expansion of an entire function converges at every finite point in the complex plane.The analytic continu...If a spatial-domain function has a finite support,its Fourier transform is an entire function.The Taylor series expansion of an entire function converges at every finite point in the complex plane.The analytic continuation theory suggests that a finite-sized object can be uniquely determined by its frequency components in a very small neighborhood.Trying to obtain such an exact Taylor expansion is difficult.This paper proposes an iterative algorithm to extend the measured frequency components to unmeasured regions.Computer simulations show that the proposed algorithm converges very slowly,indicating that the problem is too ill-posed to be practically solvable using available methods.展开更多
We introduce a general iterative scheme for angle-limited image reconstruction based on Landwebet＇s method. We derive a representation formula for this scheme and consequently establish its convergence conditions. Ou...We introduce a general iterative scheme for angle-limited image reconstruction based on Landwebet＇s method. We derive a representation formula for this scheme and consequently establish its convergence conditions. Our results suggest certain relaxation strategies for an accelerated convergence for angle-limited image reconstruction in L^2-norm comparing with alternative projection methods. The convolution-backprojection algorithm is given for this iterative process.展开更多
We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization(ML-EM)algorithm.In this study,we extend these algorithms to Bayesian algorithms...We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization(ML-EM)algorithm.In this study,we extend these algorithms to Bayesian algorithms.The family of emission-EM-lookalike algorithms utilizes a multiplicative update scheme.The extension of these algorithms to Bayesian algorithms is achieved by introducing a new simple factor,which contains the Bayesian information.One of the extended algorithms can be applied to emission tomography and another to transmission tomography.Computer simulations are performed and compared with the corresponding un-extended algorithms.The total-variation norm is employed as the Bayesian constraint in the computer simulations.The newly developed algorithms demonstrate a stable performance.A simple Bayesian algorithm can be derived for any noise variance function.The proposed algorithms have properties such as multiplicative updating,non-negativity,faster convergence rates for bright objects,and ease of implementation.Our algorithms are inspired by Green’s one-steplate algorithm.If written in additive-update form,Green’s algorithm has a step size determined by the future image value,which is an undesirable feature that our algorithms do not have.展开更多
基金supported by the NationalNatural Science Foundation of China(Grant No.11902253)the Fundamental Research Funds for the Central Universities of China.The authors are grateful for this support.
文摘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.
文摘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.
基金supported by the National High Technology Research and Development Program of China(Grant No.2012AA011603)the National Natural Science Foundation of China(Grant No.61372172)
文摘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.
文摘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.
基金National Natural Science Foundation of China(No.61171179,No.61171178)Natural Science Foundation of Shanxi Province(No.2010011002-1,No.2010011002-2and No.2012021011-2)
文摘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.
基金This research is partially supported by NIH,No.R15EB024283.
文摘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.
文摘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.
基金This research is partially supported by NIH,No.R15EB024283.
文摘If a spatial-domain function has a finite support,its Fourier transform is an entire function.The Taylor series expansion of an entire function converges at every finite point in the complex plane.The analytic continuation theory suggests that a finite-sized object can be uniquely determined by its frequency components in a very small neighborhood.Trying to obtain such an exact Taylor expansion is difficult.This paper proposes an iterative algorithm to extend the measured frequency components to unmeasured regions.Computer simulations show that the proposed algorithm converges very slowly,indicating that the problem is too ill-posed to be practically solvable using available methods.
基金Supported by the National Natural Science Foundation of China(No.60772041,60325101,60272018,60628102)the National Key Basic Research Special Foundation(2003CB716101),Ministry of Education(306017)Engineering Research Institute of Peking University,and Microsoft Research Asia.
文摘We introduce a general iterative scheme for angle-limited image reconstruction based on Landwebet＇s method. We derive a representation formula for this scheme and consequently establish its convergence conditions. Our results suggest certain relaxation strategies for an accelerated convergence for angle-limited image reconstruction in L^2-norm comparing with alternative projection methods. The convolution-backprojection algorithm is given for this iterative process.
基金This research is partially supported by NIH(No.R15EB024283).
文摘We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization(ML-EM)algorithm.In this study,we extend these algorithms to Bayesian algorithms.The family of emission-EM-lookalike algorithms utilizes a multiplicative update scheme.The extension of these algorithms to Bayesian algorithms is achieved by introducing a new simple factor,which contains the Bayesian information.One of the extended algorithms can be applied to emission tomography and another to transmission tomography.Computer simulations are performed and compared with the corresponding un-extended algorithms.The total-variation norm is employed as the Bayesian constraint in the computer simulations.The newly developed algorithms demonstrate a stable performance.A simple Bayesian algorithm can be derived for any noise variance function.The proposed algorithms have properties such as multiplicative updating,non-negativity,faster convergence rates for bright objects,and ease of implementation.Our algorithms are inspired by Green’s one-steplate algorithm.If written in additive-update form,Green’s algorithm has a step size determined by the future image value,which is an undesirable feature that our algorithms do not have.
