上海交通大学学报（英文版） ›› 2017, Vol. 22 ›› Issue (6): 756-762.doi: 10.1007/s12204-017-1897-2

• • 上一篇    

CT Reconstruction with Priori MRI Images Through Multi-Group Datasets Expansion

WANG Qihui (王齐辉), XI Yan (奚岩), CHEN Yi (陈毅),ZHANG Weikang (张伟康), ZHAO Jun* (赵俊)   

  1. (School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
  • 出版日期:2017-12-01 发布日期:2017-12-03
  • 通讯作者: ZHAO Jun (赵俊) E-mail:junzhao@sjtu.edu.cn

CT Reconstruction with Priori MRI Images Through Multi-Group Datasets Expansion

WANG Qihui (王齐辉), XI Yan (奚岩), CHEN Yi (陈毅),ZHANG Weikang (张伟康), ZHAO Jun* (赵俊)   

  1. (School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
  • Online:2017-12-01 Published:2017-12-03
  • Contact: ZHAO Jun (赵俊) E-mail:junzhao@sjtu.edu.cn

摘要： Computed tomography (CT) reconstruction with a well-registered priori magnetic resonance imaging (MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.

关键词: multi-group datasets expanding, computed tomography (CT), priori magnetic regonance imaging (MRI) images

Abstract: Computed tomography (CT) reconstruction with a well-registered priori magnetic resonance imaging (MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.

Key words: multi-group datasets expanding, computed tomography (CT), priori magnetic regonance imaging (MRI) images

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