基于运动估计的肺4D-CT图像冠矢状面超分辨率重建

doi:10.3969/j.issn.0258-8021. 2014. 02.006

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Abstract Lung 4D-CT plays an important role in the lung cancer radiotherapy. However, due to the great interslice thickness, the coronal and sagittal images of each 3D phase data are with low resolution. In this paper, we proposed a superresolution reconstruction method based on motion estimation to improve the image resolution of lung coronal and sagittal images. First, we analyzed the basic model of image degradation. Then, we employed motion estimation method based on full search block matching algorithm to estimate images motion vector field of different “frames”. At last, based on the motion vector field, we employed the iterative back projection (IBP) algorithm to reconstruct highresolution lung coronal and sagittal images. We used a public available dataset to evaluate the proposed algorithm, the dataset consists of 10 groups of lung 4D-CT data, each group contains 10 phases. We selected sagittal images of different phases to perform above procedure for experiments. Experimental results showed that the proposed algorithm significantly decreases edge widths（5.41±0.60, P<0.001）compared with the conventional methods, for instance, the nearest interpolation is 9.93±0.59 and bilinear interpolation is 8.04SymbolqB@069, which significantly increases image average gradient value compared with bilinear interpolation (7.49±0.75 vs 5.41±0.59, P<0.001）. Our method yields superior performance both qualitatively and quantitatively.

Key words： 4D-CT lung data coronal and sagittal image superresolution reconstruction motion estimation IBP algorithm

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	XIAO Shan WANG Ting Ting ZHANG Yu^*

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XIAO Shan WANG Ting Ting ZHANG Yu^*. SuperResolution Reconstruction for Lung 4D-CT Coronal and Sagittal Image Based on Motion Estimation[J]. journal1, 2014, 33(2): 170-177.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2014. 02.006 OR http://cjbme.csbme.org/EN/Y2014/V33/I2/170

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