Estimating the 4D Respiratory Lung Motion by Spatiotemporal Registration and Building Super-Resolution Image

  • Guorong Wu
  • Qian Wang
  • Jun Lian
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)


The estimation of lung motion in 4D-CT with respect to the respiratory phase becomes more and more important for radiation therapy of lung cancer. Modern CT scanner can only scan a limited region of body at each couch table position. Thus, motion artifacts due to the patient’s free breathing during scan are often observable in 4D-CT, which could undermine the procedure of correspondence detection in the registration. Another challenge of motion estimation in 4D-CT is how to keep the lung motion consistent over time. However, the current approaches fail to meet this requirement since they usually register each phase image to a pre-defined phase image independently, without considering the temporal coherence in 4D-CT. To overcome these limitations, we present a unified approach to estimate the respiratory lung motion with two iterative steps. First, we propose a new spatiotemporal registration algorithm to align all phase images of 4D-CT (in low-resolution) onto a high-resolution group-mean image in the common space. The temporal consistency is persevered by introducing the concept of temporal fibers for delineating the spatiotemporal behavior of lung motion along the respiratory phase. Second, the idea of super resolution is utilized to build the group-mean image with more details, by integrating the highly-redundant image information contained in the multiple respiratory phases. Accordingly, by establishing the correspondence of each phase image w.r.t. the high-resolution group-mean image, the difficulty of detecting correspondences between original phase images with missing structures is greatly alleviated, thus more accurate registration results can be achieved. The performance of our proposed 4D motion estimation method has been extensively evaluated on a public lung dataset. In all experiments, our method achieves more accurate and consistent results in lung motion estimation than all other state-of-the-art approaches.


Motion Estimation Phase Image Respiratory Motion Attribute Vector Temporal Consistency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guorong Wu
    • 1
  • Qian Wang
    • 1
  • Jun Lian
    • 2
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North CarolinaChapel HillUnited States
  2. 2.Department of Radiation PhysicsUniversity of North CarolinaChapel HillUnited States

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