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

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【作者】 王齐辉奚岩陈毅张伟康赵俊

【Author】 WANG Qihui;XI Yan;CHEN Yi;ZHANG Weikang;ZHAO Jun;School of Biomedical Engineering,Shanghai Jiao Tong University;

【机构】 School of Biomedical Engineering,Shanghai Jiao Tong University

【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.

【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.

【基金】 the National Natural Science Foundation of China(No.813716234);the National Basic Research Program(973)of China(No.2010CB834302);Shanghai Jiao Tong University Medical Engineering Cross Research Funds(Nos.YG2013MS30 and YG2014ZD05)
  • 【文献出处】 Journal of Shanghai Jiaotong University(Science) ,上海交通大学学报(英文版) , 编辑部邮箱 ,2017年06期
  • 【分类号】R814.42;TP391.41
  • 【被引频次】2
  • 【下载频次】25
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