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

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

doi:10.1007/978-3-642-33415-3_27

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
Editors	Nicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
Publisher	Springer Verlag
Pages	214-222
Number of pages	9
ISBN (Print)	9783642334146
DOIs	https://doi.org/10.1007/978-3-642-33415-3_27
Publication status	Published - 2012
Event	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France Duration: 2012 Oct 1 → 2012 Oct 5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7510 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	12/10/1 → 12/10/5

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-33415-3_27

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 N. Ayache, H. Delingette, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings (pp. 214-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7510 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33415-3_27

Non-local means resolution enhancement of lung 4D-CT data. / Zhang, Yu; Wu, Guorong; Yap, Pew Thian et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. ed. / Nicholas Ayache; Herve Delingette; Polina Golland; Kensaku Mori. Springer Verlag, 2012. p. 214-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7510 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, Y, Wu, G, Yap, PT, Feng, Q, Lian, J, Chen, W & Shen, D 2012, Non-local means resolution enhancement of lung 4D-CT data. in N Ayache, H Delingette, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7510 LNCS, Springer Verlag, pp. 214-222, 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012, Nice, France, 12/10/1. https://doi.org/10.1007/978-3-642-33415-3_27

Zhang Y, Wu G, Yap PT, Feng Q, Lian J, Chen W et al. Non-local means resolution enhancement of lung 4D-CT data. In Ayache N, Delingette H, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 214-222. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33415-3_27

Zhang, Yu ; Wu, Guorong ; Yap, Pew Thian et al. / Non-local means resolution enhancement of lung 4D-CT data. Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. editor / Nicholas Ayache ; Herve Delingette ; Polina Golland ; Kensaku Mori. Springer Verlag, 2012. pp. 214-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

UN SDGs

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