Non-local means resolution enhancement of lung 4D-CT data.

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

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

Image resolution in 4D-CT is a crucial bottleneck that needs to be overcome for improved dose planning in radiotherapy for lung cancer. In this paper, we propose a novel patch-based algorithm to enhance the image quality of 4D-CT data. Our premise is that anatomical information missing in one phase can be recovered from complementary information embedded in other phases. We employ a patch-based mechanism to propagate information across phases for reconstruction of intermediate slices in the axial direction, where resolution is normally the lowest. Specifically, structurally-matching and spatially-nearby patches are combined for reconstruction of each patch. For greater sensitivity to anatomical nuances, we further employ a quad-tree technique to adaptively partition each slice of the image in each phase for more fine-grained refinement. Our evaluation based on a public 4D-CT lung data indicates that our algorithm gives very promising results with significantly enhanced image structures.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages214-222
Number of pages9
Volume15
EditionPt 1
Publication statusPublished - 2012 Dec 1

ASJC Scopus subject areas

  • Medicine(all)

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  • Cite this

    Zhang, Y., Wu, G., Yap, P. T., Feng, Q., Lian, J., Chen, W., & Shen, D. (2012). Non-local means resolution enhancement of lung 4D-CT data. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 15, pp. 214-222)