Abstract: Four-dimensional computer tomography (4D-CT) has a great value in lung cancer radiotherapy for its capability in providing lung information with respiratory motion. We employed a global graph cuts super-resolution (SR) reconstruction method to reconstruct high-resolution lung 4D-CT images. First, the high-resolution images reconstruction energy function was built based on a Maximum a posteriori Markov Random Field (MAP-MRF) formulation. The energy function was then transformed to a graph formulation, which was solved using graph cut algorithm. All the evaluation results showed that this approach outperformed the line interpolation and projection onto convex sets (POCS) approach with an improved structural clarity.