基于分块的肺4DCT图像超分辨率重建

doi:10.3969/j.issn.0258-8021.2015.02.005

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Abstract Lung 4DCT is of great values in the application of tumor localization and individualized precise radiotherapy. However, due to the dose limitation, 4DCT data are in low resolution. In this paper, we proposed a patchbased superresolution reconstruction approach to enhance the resolution of 4DCT images. First, we divided the images into a number of overlapping patches. Then, Active Demons registration algorithm was utilized to adaptively select corresponding patches in each phase for the specific patch, and obtain the motion fields between the patches simultaneously. After that, iterative back projection (IBP) algorithm was adopted to reconstruct highresolution patch. At last, we assembled all patches into a final output high resolution image. A public dataset provided by the DIRlab at the University of Texas M.D. Anderson Cancer Center (Houston, TX) was employed to evaluate the proposed method. The dataset consists of 10 groups of lung 4DCT data, each group contains 10 phases. Images of different phases are selected to perform the above procedure for experiment. Quantitative evaluation results indicate that the proposed method significantly increases image average gradient value (892±050, P<0001) compared with back projection (BP) algorithm (765±044) and global IBP algorithm (792±043). Qualitative evaluation results also show that our approach can eliminate artifacts effectively and get clearer lung 4DCT images with significantly enhanced structures.

Key words： lung 4DCT data patch adaptive selection superresolution reconstruction

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	Wang Tingting Wu Xiuxiu Zhang Yu^*

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Wang Tingting Wu Xiuxiu Zhang Yu^*. PatchBased SuperResolution Reconstruction for Lung 4DCT Images[J]. journal1, 2015, 34(2): 160-167.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2015.02.005 OR http://cjbme.csbme.org/EN/Y2015/V34/I2/160

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