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
Externally published	Yes
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	France
City	Nice
Period	12/10/1 → 12/10/5

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Access to Document

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

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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; Feng, Qianjin; Lian, Jun; Chen, Wufan; Shen, Dinggang.

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)). https://doi.org/10.1007/978-3-642-33415-3_27

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

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UN SDGs

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