Variational Optical Flow Estimation Based Super-Resolution Reconstruction for Lung 4D-CT Image

The acquisition of lung 4D computed tomography (4D-CT) data is limited by the scanning time and radiation dose, which leads to the sampling rate in the axial direction is much less than that in the in-plane direction. In order to get better quality of 4D-CT images, based on the inherent self-similarity of medical images, a new method of image sequence super-resolution reconstruction is proposed in this paper. This method uses the local and global variational optical flow estimation to improve the quality of enlarged 4D-CT image. Firstly, we present a combined local and global variational optical flow model, in order to estimate the motion fields (i.e., the optical flow fields) between different phases in the corresponding positions. Then, the optical flow field is obtained by solving the model with the fast alternating direction method of multiplier. Finally, according to the calculated motion fields, we employ the improved non-local iterative back projection (NLIBP) algorithm to reconstruct high resolution lung images. The experimental results have shown that, in both quantification standard and visual perception, this method outperforms non-local iterative back projection algorithm and full search block matching based iterative back projection technique. Furthermore, our method can generate clear edges while enhancing the texture of images.