Reconstruction of super-resolution lung 4D-CT using patch-based sparse representation

Yu Zhang, Guorong Wu, Pew Thian Yap, Qianjin Feng, Jun Lian, Wufan Chen, Dinggang Shen

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

13 Citations (Scopus)

Abstract

4D-CT plays an important role in lung cancer treatment. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical. As a result, artifacts such as lung vessel discontinuity and partial volume are typical in 4D-CT images and might mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for super-resolution enhancement of the 4D-CT images along the superior-inferior direction. Our working premise is that the anatomical information that is missing at one particular phase can be recovered from other phases. Based on this assumption, we employ a patch-based mechanism for guided reconstruction of super-resolution axial slices. Specifically, to reconstruct each targeted super-resolution slice for a CT image at a particular phase, we agglomerate a dictionary of patches from images of all other phases in the 4D-CT sequence. Then we perform a sparse combination of the patches in this dictionary to reconstruct details of a super-resolution patch, under constraint of similarity to the corresponding patches in the neighboring slices. By iterating this procedure over all possible patch locations, a superresolution 4D-CT image sequence with enhanced anatomical details can be eventually reconstructed. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in terms of preserving image details and suppressing misleading artifacts.

Original languageEnglish
Title of host publication2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Pages925-931
Number of pages7
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 - Providence, RI, United States
Duration: 2012 Jun 162012 Jun 21

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
CountryUnited States
CityProvidence, RI
Period12/6/1612/6/21

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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