Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence ...Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.展开更多
Muon tomography is a novel method for the non-destructive imaging of materials based on muon rays,which are highly penetrating in natural background radiation.Currently,the most commonly used imaging methods include m...Muon tomography is a novel method for the non-destructive imaging of materials based on muon rays,which are highly penetrating in natural background radiation.Currently,the most commonly used imaging methods include muon radiography and muon tomography.A previously studied method known as coinciding muon trajectory density tomography,which utilizes muonic secondary particles,is proposed to image low and medium atomic number(Z)materials.However,scattering tomography is mostly used to image high-Z materials,and coinciding muon trajectory density tomography exhibits a hollow phenomenon in the imaging results owing to the self-absorption effect.To address the shortcomings of the individual imaging methods,hybrid model tomography combining scattering tomography and coinciding muon trajectory density tomography is proposed and verified.In addition,the peak signal-to-noise ratio was introduced to quantitatively analyze the image quality.Different imaging models were simulated using the Geant4 toolkit to confirm the advantages of this innovative method.The simulation results showed that hybrid model tomography can image centimeter-scale materials with low,medium,and high Z simultaneously.For high-Z materials with similar atomic numbers,this method can clearly distinguish those with apparent differences in density.According to the peak signal-to-noise ratio of the analysis,the reconstructed image quality of the new method was significantly higher than that of the individual imaging methods.This study provides a reliable approach to the compatibility of scattering tomography and coinciding muon trajectory density tomography.展开更多
With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper d...With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.展开更多
The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce t...The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.展开更多
Hyperthermia has proven to be beneficial to treating superficial malignancies, particularly chest wall recurrences of breast cancer. During hyperthermia, monitoring the time–temperature profiles in the target and sur...Hyperthermia has proven to be beneficial to treating superficial malignancies, particularly chest wall recurrences of breast cancer. During hyperthermia, monitoring the time–temperature profiles in the target and surrounding areas is of great significance for the effect of therapy. An ultrasound-based temperature imaging method has advantages over other approaches. When the temperature around the tumor is calculated by using the propagation speed of ultrasound, there always exist overshoot artifacts along the boundary between different tissues. In this paper, we present a new method combined with empirical mode decomposition（EDM）, similarity constraint, and continuity constraint to optimize the temperature images. Simulation and phantom experiment results compared with those from our previously proposed method prove that the EMD-based method can build a better temperature field image, which can adaptively yield better temperature images with less computation for assistant medical treatment control.展开更多
基金Supported by National Key Research and Development Program of China(2016YFF0201005)。
文摘Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.
基金supported by the National Natural Science Foundation of China(No.11875163)Natural Science Foundation of Hunan Province(Nos.2021JJ20006 and 2021JJ40444)+1 种基金Ministry of Science and Technology of China(No.2020YFE0202001)Department of Education of Hunan Province(Nos.19B488 and 21A0281)。
文摘Muon tomography is a novel method for the non-destructive imaging of materials based on muon rays,which are highly penetrating in natural background radiation.Currently,the most commonly used imaging methods include muon radiography and muon tomography.A previously studied method known as coinciding muon trajectory density tomography,which utilizes muonic secondary particles,is proposed to image low and medium atomic number(Z)materials.However,scattering tomography is mostly used to image high-Z materials,and coinciding muon trajectory density tomography exhibits a hollow phenomenon in the imaging results owing to the self-absorption effect.To address the shortcomings of the individual imaging methods,hybrid model tomography combining scattering tomography and coinciding muon trajectory density tomography is proposed and verified.In addition,the peak signal-to-noise ratio was introduced to quantitatively analyze the image quality.Different imaging models were simulated using the Geant4 toolkit to confirm the advantages of this innovative method.The simulation results showed that hybrid model tomography can image centimeter-scale materials with low,medium,and high Z simultaneously.For high-Z materials with similar atomic numbers,this method can clearly distinguish those with apparent differences in density.According to the peak signal-to-noise ratio of the analysis,the reconstructed image quality of the new method was significantly higher than that of the individual imaging methods.This study provides a reliable approach to the compatibility of scattering tomography and coinciding muon trajectory density tomography.
基金Project supported by the National Basic Research Program of China(Grant No.2006CB7057005)the National High Technology Research and Development Program of China(Grant No.2009AA012200)the National Natural Science Foundation of China (Grant No.60672104)
文摘With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.
基金Project supported by the National Natural Science Foundation of China(Grant No.61372172)
文摘The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.
基金supported by the DoD/BCRP Idea Award BC095397P1the National Natural Science Foundation of China(Grant No.61201425)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Hyperthermia has proven to be beneficial to treating superficial malignancies, particularly chest wall recurrences of breast cancer. During hyperthermia, monitoring the time–temperature profiles in the target and surrounding areas is of great significance for the effect of therapy. An ultrasound-based temperature imaging method has advantages over other approaches. When the temperature around the tumor is calculated by using the propagation speed of ultrasound, there always exist overshoot artifacts along the boundary between different tissues. In this paper, we present a new method combined with empirical mode decomposition（EDM）, similarity constraint, and continuity constraint to optimize the temperature images. Simulation and phantom experiment results compared with those from our previously proposed method prove that the EMD-based method can build a better temperature field image, which can adaptively yield better temperature images with less computation for assistant medical treatment control.
