Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image

Guorong Wu, Qian Wang, Jun Lian, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages532-539
Number of pages8
Volume6891 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2011 Oct 11
Externally publishedYes
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 2011 Sep 182011 Sep 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6891 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period11/9/1811/9/22

Fingerprint

Super-resolution
Motion estimation
Image resolution
Lung
Registration
Motion
Radiotherapy
Motion Estimation
Correspondence
Fibers
High Resolution
Radiation Therapy
Lung Cancer
Experiments
Scanner
Table
Fiber
Requirements

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wu, G., Wang, Q., Lian, J., & Shen, D. (2011). Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 6891 LNCS, pp. 532-539). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6891 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-23623-5_67

Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image. / Wu, Guorong; Wang, Qian; Lian, Jun; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6891 LNCS PART 1. ed. 2011. p. 532-539 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6891 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, G, Wang, Q, Lian, J & Shen, D 2011, Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 6891 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6891 LNCS, pp. 532-539, 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011, Toronto, ON, Canada, 11/9/18. https://doi.org/10.1007/978-3-642-23623-5_67
Wu G, Wang Q, Lian J, Shen D. Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 6891 LNCS. 2011. p. 532-539. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-23623-5_67
Wu, Guorong ; Wang, Qian ; Lian, Jun ; Shen, Dinggang. / Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6891 LNCS PART 1. ed. 2011. pp. 532-539 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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