Cone-beam computed tomography(CBCT) is mostly used for position verification during the treatment process. However,severe image artifacts in CBCT hinder its direct use in dose calculation and adaptive radiation therap...Cone-beam computed tomography(CBCT) is mostly used for position verification during the treatment process. However,severe image artifacts in CBCT hinder its direct use in dose calculation and adaptive radiation therapy re-planning for proton therapy. In this study, an improved U-Net neural network named CBAM-U-Net was proposed for CBCT noise reduction in proton therapy, which is a CBCT denoised U-Net network with convolutional block attention modules. The datasets contained 20 groups of head and neck images. The CT images were registered to CBCT images as ground truth. The original CBCT denoised U-Net network, sCTU-Net, was trained for model performance comparison. The synthetic CT(SCT) images generated by CBAM-U-Net and the original sCTU-Net are called CBAM-SCT and U-Net-SCT images, respectively. The HU accuracies of the CT, CBCT, and SCT images were compared using four metrics: mean absolute error(MAE), root mean square error(RMSE), peak signal-to-noise ratio(PSNR), and structure similarity index measure(SSIM). The mean values of the MAE, RMSE, PSNR, and SSIM of CBAM-SCT images were 23.80 HU, 64.63 HU, 52.27 dB, and 0.9919, respectively,which were superior to those of the U-Net-SCT images. To evaluate dosimetric accuracy, the range accuracy was compared for a single-energy proton beam. The γ-index pass rates of a 4 cm × 4 cm scanned field and simple plan were calculated to compare the effects of the noise reduction capabilities of the original U-Net and CBAM-U-Net on the dose calculation results. CBAM-U-Net reduced noise more effectively than sCTU-Net, particularly in high-density tissues. We proposed a CBAM-U-Net model for CBCT noise reduction in proton therapy. Owing to the excellent noise reduction capabilities of CBAM-U-Net, the proposed model provided relatively explicit information regarding patient tissues. Moreover, it maybe be used in dose calculation and adaptive treatment planning in the future.展开更多
Aim To investigate the relationship between the positioning of the lower central incisor and physical morphology of the surrounding alveolar bone. Methodology Thirty-eight patients （18 males, 20 females）, with mean ...Aim To investigate the relationship between the positioning of the lower central incisor and physical morphology of the surrounding alveolar bone. Methodology Thirty-eight patients （18 males, 20 females）, with mean age of 13.4 years, were included in this study. As part of orthodontic treatment planning the patients were required to take dental Cone-beam CT （CBCT） covering the region of lower incisors, the sur- rounding alveolar bone and the mandibular symphysis. The cephalometric parameters were designed and measured to indicate the inclination of lower central incisor and physical morphology of the adjacent alveolar bone. Computer-aided descriptive statistical analysis was performed using SPSS 15.0 software package for Windows. A correlation analysis and a linear regression analysis between the incisor inclination and the alveolar bone morphology were performed. Results Significant positive correlations were found between the lower central incisor inclination and the morphological contour of the alveolar bone （P〈0.05）. The lower central incisor root apex was closer to the lingual alveolar crest when it was buccally inclined. Conclusion The morphology of the alveolar bone may be affected by incisal inclination.展开更多
Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp ...Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.展开更多
Conventional X-ray tube-based cone-beam computed tomography(CX-CBCT) systems have great potential in industrial applications. Such systems can rapidly obtain a three-dimensional(3D) image of an object.Conventional X-r...Conventional X-ray tube-based cone-beam computed tomography(CX-CBCT) systems have great potential in industrial applications. Such systems can rapidly obtain a three-dimensional(3D) image of an object.Conventional X-ray tubes fulfill the requirements for industrial applications, because of their high tube voltage and power. Continuous improvements have been made to CX-CBCT systems, such as imaging time shortening,acquisition strategy optimization, and imaging software development, etc. In this study, a CX-CBCT system is developed. Additionally, some improvements to the CX-CBCT system are proposed based on the hardware conditions of the X-ray tube and detector. A near-detector(ND)geometry condition is employed to obtain a sharper image and larger detection area. An improved acquisition strategy is proposed to simplify operations and reduce total imaging time. In the ND geometry condition, a simplified method called FBP slice stacking(SS-FBP) is proposed, which can be applied to 3D image reconstruction. SS-FBP is timesaving relative to traditional methods. Furthermore, imaging software for the CX-CBCT system is developed in the MATLAB environment. Several imaging experiments were performed. The results suggest that the CX-CBCT system works properly, and that the above improvements are feasible and practical.展开更多
Purpose: To derive a clinically-practical margin formula between clinical target volume (CTV) and planning target volume (PTV) for single-fraction stereotactic radiosurgery (SRS). Methods: In previous publications on ...Purpose: To derive a clinically-practical margin formula between clinical target volume (CTV) and planning target volume (PTV) for single-fraction stereotactic radiosurgery (SRS). Methods: In previous publications on the margin between the CTV and the PTV, a Gaussian function with zero mean was assumed for the systematic error and the machine systematic error was completely ignored. In this work we adopted a Dirac delta function for the machine systematic error for a given machine with nonzero mean systematic error. Mathematical formulas for calculating the CTV-PTV margin for single-fraction SRS treatments were proposed. Results: Margins for single fraction treatments were derived such that the CTVs received the prescribed dose in 95% of the SRS patients. The margin defined in this study was machine specific and accounted for nonzero mean systematic error. The differences between our formulas and a previously published formula were discussed. Conclusion: Clinical margin formulas were proposed for determining the margin between the CTV and the PTV in SRS treatments. Previous margin’s recipes, being derived specifically for conventional treatments, may be inappropriate for single-fraction SRS and could result in geometric miss of the target and even treatment failure for machines possessing of large systematic errors.展开更多
In order to determine the projected coordinate origin in the cone-beam CT scanning system with respect to the Feldkamp-Davis-Kress （FDK） algorithm, we propose a simple yet feasible method to accurately measure the p...In order to determine the projected coordinate origin in the cone-beam CT scanning system with respect to the Feldkamp-Davis-Kress （FDK） algorithm, we propose a simple yet feasible method to accurately measure the projected coordinate origin. This method was established on the basis of the theory that the projection of a spherical object in the cone-beam field is an ellipse. We first utilized image processing and the least square estimation method to get each major axis of the elliptical Digital Radiography （DR） projections of a group of spherical objects. Then we determined the intersection point of the group of major axis by solving an over-determined equation set that was composed by the major axis equations of all the elliptical projections. Based on the experimental results, this new method was proved to be easy to implement in practical scanning systems with high accuracy and anti-noise capability.展开更多
X-ray cone-beam computed tomography （CT） has such notable features as high efficiency and precision, and is widely used in the fields of medical imaging and industrial non-destructive testing, but the inherent imagi...X-ray cone-beam computed tomography （CT） has such notable features as high efficiency and precision, and is widely used in the fields of medical imaging and industrial non-destructive testing, but the inherent imaging degradation reduces the quality of CT images. Aimed at the problems of projection image degradation and restoration in cone-beam CT, a point spread function （PSF） modeling method is proposed first. The general PSF model of cone- beam CT is established, and based on it, the PSF under arbitrary scanning conditions can be calculated directly for projection image restoration without the additional measurement, which greatly improved the application convenience of cone-beam CT. Secondly, a projection image restoration algorithm based on pre-filtering and pre-segmentation is proposed, which can make the edge contours in projection images and slice images clearer after restoration, and control the noise in the equivalent level to the original images. Finally, the experiments verified the feasibility and effectiveness of the proposed methods.展开更多
For a cone-beam three-dimensional computed tomography （3D-CT） scanning system, voxel size is an important indicator to guarantee the accuracy of data analysis and feature measurement based on 3D-CT images. Meanwhile...For a cone-beam three-dimensional computed tomography （3D-CT） scanning system, voxel size is an important indicator to guarantee the accuracy of data analysis and feature measurement based on 3D-CT images. Meanwhile, the voxel size changes with the movement of the rotary stage along X-ray direction. In order to realize the automatic calibration of the voxel size, a new and easily-implemented method is proposed. According to this method, several projections of a spherical phantom are captured at different imaging positions and the corresponding voxel size values are calculated by non-linear least-square fitting. Through these interpolation values, a linear equation is obtained that reflects the relationship between the voxel size and the rotary stage translation distance from its nominal zero position. Finally, the linear equation is imported into the calibration module of the 3D-CT scanning system. When the rotary stage is moving along X-ray direction, the accurate value of the voxel size is dynamically exported. The experimental results prove that this method meets the requirements of the actual CT scanning system, and has virtues of easy implementation and high accuracy.展开更多
A direct filtered-backprojection （FBP） reconstruction algorithm is presented for circular cone-beam computed tomography （CB-CT） that allows the filter operation to be applied efficiently with shift-variant band-pa...A direct filtered-backprojection （FBP） reconstruction algorithm is presented for circular cone-beam computed tomography （CB-CT） that allows the filter operation to be applied efficiently with shift-variant band-pass characteristics on the kernel function. Our algorithm is derived from the ramp-filter based FBP method of Feldkamp et al. and obtained by decomposing the ramp filtering into a convolution involving the Hilbert kernel （global operation） and a subsequent differentiation operation （local operation）. The differentiation is implemented as a finite difference of two （Hilbert filtered） data samples and carried out as part of the backprojection step. The spacing between the two samples, which defines the low-pass characteristics of the filter operation, can thus be selected individually for each point in the image volume. We here define the sample spacing to follow the magnification of the divergent-beam geometry and thus obtain a novel, depth-dependent filtering algorithm for circular CB-CT. We evaluate this resulting algorithm using computer-simulated CB data and demonstrate that our algorithm yields results where spatial resolution and image noise are distributed much more uniformly over the field-of-view, compared to Feldkamp＇s approach.展开更多
基金Digital Medical Equipment Research and Development Project,Ministry of Science and Technology,China:The development of Synchrotron-based proton therapy system(2016YFC0105400).
文摘Cone-beam computed tomography(CBCT) is mostly used for position verification during the treatment process. However,severe image artifacts in CBCT hinder its direct use in dose calculation and adaptive radiation therapy re-planning for proton therapy. In this study, an improved U-Net neural network named CBAM-U-Net was proposed for CBCT noise reduction in proton therapy, which is a CBCT denoised U-Net network with convolutional block attention modules. The datasets contained 20 groups of head and neck images. The CT images were registered to CBCT images as ground truth. The original CBCT denoised U-Net network, sCTU-Net, was trained for model performance comparison. The synthetic CT(SCT) images generated by CBAM-U-Net and the original sCTU-Net are called CBAM-SCT and U-Net-SCT images, respectively. The HU accuracies of the CT, CBCT, and SCT images were compared using four metrics: mean absolute error(MAE), root mean square error(RMSE), peak signal-to-noise ratio(PSNR), and structure similarity index measure(SSIM). The mean values of the MAE, RMSE, PSNR, and SSIM of CBAM-SCT images were 23.80 HU, 64.63 HU, 52.27 dB, and 0.9919, respectively,which were superior to those of the U-Net-SCT images. To evaluate dosimetric accuracy, the range accuracy was compared for a single-energy proton beam. The γ-index pass rates of a 4 cm × 4 cm scanned field and simple plan were calculated to compare the effects of the noise reduction capabilities of the original U-Net and CBAM-U-Net on the dose calculation results. CBAM-U-Net reduced noise more effectively than sCTU-Net, particularly in high-density tissues. We proposed a CBAM-U-Net model for CBCT noise reduction in proton therapy. Owing to the excellent noise reduction capabilities of CBAM-U-Net, the proposed model provided relatively explicit information regarding patient tissues. Moreover, it maybe be used in dose calculation and adaptive treatment planning in the future.
文摘Aim To investigate the relationship between the positioning of the lower central incisor and physical morphology of the surrounding alveolar bone. Methodology Thirty-eight patients （18 males, 20 females）, with mean age of 13.4 years, were included in this study. As part of orthodontic treatment planning the patients were required to take dental Cone-beam CT （CBCT） covering the region of lower incisors, the sur- rounding alveolar bone and the mandibular symphysis. The cephalometric parameters were designed and measured to indicate the inclination of lower central incisor and physical morphology of the adjacent alveolar bone. Computer-aided descriptive statistical analysis was performed using SPSS 15.0 software package for Windows. A correlation analysis and a linear regression analysis between the incisor inclination and the alveolar bone morphology were performed. Results Significant positive correlations were found between the lower central incisor inclination and the morphological contour of the alveolar bone （P〈0.05）. The lower central incisor root apex was closer to the lingual alveolar crest when it was buccally inclined. Conclusion The morphology of the alveolar bone may be affected by incisal inclination.
基金supported by the National Natural Science Foundation of China(Nos.61771279,11435007)the National Key Research and Development Program of China(No.2016YFF0101304)
文摘Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.
基金supported by the Fundamental Research Funds for the Central Universities(Nos.lzujbky-2016-208 and lzujbky-2016-32)
文摘Conventional X-ray tube-based cone-beam computed tomography(CX-CBCT) systems have great potential in industrial applications. Such systems can rapidly obtain a three-dimensional(3D) image of an object.Conventional X-ray tubes fulfill the requirements for industrial applications, because of their high tube voltage and power. Continuous improvements have been made to CX-CBCT systems, such as imaging time shortening,acquisition strategy optimization, and imaging software development, etc. In this study, a CX-CBCT system is developed. Additionally, some improvements to the CX-CBCT system are proposed based on the hardware conditions of the X-ray tube and detector. A near-detector(ND)geometry condition is employed to obtain a sharper image and larger detection area. An improved acquisition strategy is proposed to simplify operations and reduce total imaging time. In the ND geometry condition, a simplified method called FBP slice stacking(SS-FBP) is proposed, which can be applied to 3D image reconstruction. SS-FBP is timesaving relative to traditional methods. Furthermore, imaging software for the CX-CBCT system is developed in the MATLAB environment. Several imaging experiments were performed. The results suggest that the CX-CBCT system works properly, and that the above improvements are feasible and practical.
文摘Purpose: To derive a clinically-practical margin formula between clinical target volume (CTV) and planning target volume (PTV) for single-fraction stereotactic radiosurgery (SRS). Methods: In previous publications on the margin between the CTV and the PTV, a Gaussian function with zero mean was assumed for the systematic error and the machine systematic error was completely ignored. In this work we adopted a Dirac delta function for the machine systematic error for a given machine with nonzero mean systematic error. Mathematical formulas for calculating the CTV-PTV margin for single-fraction SRS treatments were proposed. Results: Margins for single fraction treatments were derived such that the CTVs received the prescribed dose in 95% of the SRS patients. The margin defined in this study was machine specific and accounted for nonzero mean systematic error. The differences between our formulas and a previously published formula were discussed. Conclusion: Clinical margin formulas were proposed for determining the margin between the CTV and the PTV in SRS treatments. Previous margin’s recipes, being derived specifically for conventional treatments, may be inappropriate for single-fraction SRS and could result in geometric miss of the target and even treatment failure for machines possessing of large systematic errors.
基金Supported by the National Natural Science Foundation of China (60872080)Peking Educational Committee Corporate Construction Project
文摘In order to determine the projected coordinate origin in the cone-beam CT scanning system with respect to the Feldkamp-Davis-Kress （FDK） algorithm, we propose a simple yet feasible method to accurately measure the projected coordinate origin. This method was established on the basis of the theory that the projection of a spherical object in the cone-beam field is an ellipse. We first utilized image processing and the least square estimation method to get each major axis of the elliptical Digital Radiography （DR） projections of a group of spherical objects. Then we determined the intersection point of the group of major axis by solving an over-determined equation set that was composed by the major axis equations of all the elliptical projections. Based on the experimental results, this new method was proved to be easy to implement in practical scanning systems with high accuracy and anti-noise capability.
基金Supported by National Science and Technology Major Project of the Ministry of Industry and Information Technology of China(2012ZX04007021)Young Scientists Fund of National Natural Science Foundation of China(51105315)+1 种基金Natural Science Basic Research Program of Shaanxi Province of China(2013JM7003)Northwestern Polytechnical University Foundation for Fundamental Research(JC20120226,3102014KYJD022)
文摘X-ray cone-beam computed tomography （CT） has such notable features as high efficiency and precision, and is widely used in the fields of medical imaging and industrial non-destructive testing, but the inherent imaging degradation reduces the quality of CT images. Aimed at the problems of projection image degradation and restoration in cone-beam CT, a point spread function （PSF） modeling method is proposed first. The general PSF model of cone- beam CT is established, and based on it, the PSF under arbitrary scanning conditions can be calculated directly for projection image restoration without the additional measurement, which greatly improved the application convenience of cone-beam CT. Secondly, a projection image restoration algorithm based on pre-filtering and pre-segmentation is proposed, which can make the edge contours in projection images and slice images clearer after restoration, and control the noise in the equivalent level to the original images. Finally, the experiments verified the feasibility and effectiveness of the proposed methods.
基金Supported by National Natural Science Foundation of China(NSFC)(11275019,21106158,61077011)National State Key Laboratory of Multiphase Complex Systems(MPCS-2011-D-03)+1 种基金National Key Technology R&D Program of China(2011BAI02B02)Beijing Municipal Commission of Education(BMCE)Under the Joint-Building Project and National Key Scientific Apparatus Development of Special Item of China(2013YQ240803)
文摘For a cone-beam three-dimensional computed tomography （3D-CT） scanning system, voxel size is an important indicator to guarantee the accuracy of data analysis and feature measurement based on 3D-CT images. Meanwhile, the voxel size changes with the movement of the rotary stage along X-ray direction. In order to realize the automatic calibration of the voxel size, a new and easily-implemented method is proposed. According to this method, several projections of a spherical phantom are captured at different imaging positions and the corresponding voxel size values are calculated by non-linear least-square fitting. Through these interpolation values, a linear equation is obtained that reflects the relationship between the voxel size and the rotary stage translation distance from its nominal zero position. Finally, the linear equation is imported into the calibration module of the 3D-CT scanning system. When the rotary stage is moving along X-ray direction, the accurate value of the voxel size is dynamically exported. The experimental results prove that this method meets the requirements of the actual CT scanning system, and has virtues of easy implementation and high accuracy.
基金Supported in part by the US National Institute of Health (Nos.R01EB007236 and R21EB009168)
文摘A direct filtered-backprojection （FBP） reconstruction algorithm is presented for circular cone-beam computed tomography （CB-CT） that allows the filter operation to be applied efficiently with shift-variant band-pass characteristics on the kernel function. Our algorithm is derived from the ramp-filter based FBP method of Feldkamp et al. and obtained by decomposing the ramp filtering into a convolution involving the Hilbert kernel （global operation） and a subsequent differentiation operation （local operation）. The differentiation is implemented as a finite difference of two （Hilbert filtered） data samples and carried out as part of the backprojection step. The spacing between the two samples, which defines the low-pass characteristics of the filter operation, can thus be selected individually for each point in the image volume. We here define the sample spacing to follow the magnification of the divergent-beam geometry and thus obtain a novel, depth-dependent filtering algorithm for circular CB-CT. We evaluate this resulting algorithm using computer-simulated CB data and demonstrate that our algorithm yields results where spatial resolution and image noise are distributed much more uniformly over the field-of-view, compared to Feldkamp＇s approach.
