As a complement to X-ray computed tomography(CT),neutron tomography has been extensively used in nuclear engineer-ing,materials science,cultural heritage,and industrial applications.Reconstruction of the attenuation m...As a complement to X-ray computed tomography(CT),neutron tomography has been extensively used in nuclear engineer-ing,materials science,cultural heritage,and industrial applications.Reconstruction of the attenuation matrix for neutron tomography with a traditional analytical algorithm requires hundreds of projection views in the range of 0°to 180°and typically takes several hours to complete.Such a low time-resolved resolution degrades the quality of neutron imaging.Decreasing the number of projection acquisitions is an important approach to improve the time resolution of images;however,this requires efficient reconstruction algorithms.Therefore,sparse-view reconstruction algorithms in neutron tomography need to be investigated.In this study,we investigated the three-dimensional reconstruction algorithm for sparse-view neu-tron CT scans.To enhance the reconstructed image quality of neutron CT,we propose an algorithm that uses OS-SART to reconstruct images and a split Bregman to solve for the total variation(SBTV).A comparative analysis of the performances of each reconstruction algorithm was performed using simulated and actual experimental data.According to the analyzed results,OS-SART-SBTV is superior to the other algorithms in terms of denoising,suppressing artifacts,and preserving detailed structural information of images.展开更多
Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill ...Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFB1902700)the National Natural Science Foundation of China(No.11875129)+3 种基金the Fund of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect(No.SKLIPR1810)the Fund of Innovation Center of Radiation Application(No.KFZC2020020402)the Fund of the State Key Laboratory of Nuclear Physics and Technology,Peking University(No.NPT2020KFY08)the Joint Innovation Fund of China National Uranium Co.,Ltd.,State Key Laboratory of Nuclear Resources and Environment,East China University of Technology(No.2022NRE-LH-02).
文摘As a complement to X-ray computed tomography(CT),neutron tomography has been extensively used in nuclear engineer-ing,materials science,cultural heritage,and industrial applications.Reconstruction of the attenuation matrix for neutron tomography with a traditional analytical algorithm requires hundreds of projection views in the range of 0°to 180°and typically takes several hours to complete.Such a low time-resolved resolution degrades the quality of neutron imaging.Decreasing the number of projection acquisitions is an important approach to improve the time resolution of images;however,this requires efficient reconstruction algorithms.Therefore,sparse-view reconstruction algorithms in neutron tomography need to be investigated.In this study,we investigated the three-dimensional reconstruction algorithm for sparse-view neu-tron CT scans.To enhance the reconstructed image quality of neutron CT,we propose an algorithm that uses OS-SART to reconstruct images and a split Bregman to solve for the total variation(SBTV).A comparative analysis of the performances of each reconstruction algorithm was performed using simulated and actual experimental data.According to the analyzed results,OS-SART-SBTV is superior to the other algorithms in terms of denoising,suppressing artifacts,and preserving detailed structural information of images.
基金sponsored by the Institute of Information Technology(Vietnam Academy of Science and Technology)with Project Code“CS24.01”.
文摘Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.
