Estimating lung respiratory motion using combined global and local statistical models

Zhong Xue; Ramiro Pino; Bin Teh

doi:10.1007/978-3-319-47118-1_17

Estimating lung respiratory motion using combined global and local statistical models

Zhong Xue, Ramiro Pino, Bin Teh

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

1 Scopus citations

Abstract

In image-guided therapy of lung cancer, discrepancies between pre-procedural 3D or 4D-CT and patient’s current lung shape could reduce the accuracy for guidance. While real-time imaging is often not available, it is necessary to estimate the lung motion from real-time measurable signals such as on-chest/abdominal fiducials or surface. Traditional regression models take the entire lung motion deformation as a whole and perform the estimation in a global manner. Given high dimensionality and complexity of the lung motion patterns, the correlation of lung motion in different local areas with the surface motion could be different. Therefore, we propose a combined global and local statistics-based estimation to improve the estimation accuracy because local deformations have similar patterns and could have higher correlation with the surface motion. Results with 37 4D-CT datasets suggest local motion estimation further improves the performance for lung respiratory motion modeling.

Original language	English (US)
Title of host publication	Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings
Editors	Pierrick Coupe, Brent C. Munsell, Daniel Rueckert, Yiqiang Zhan, Guorong Wu
Publisher	Springer-Verlag
Pages	133-140
Number of pages	8
ISBN (Print)	9783319471174
DOIs	https://doi.org/10.1007/978-3-319-47118-1_17
State	Published - 2016
Event	2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: Oct 17 2016 → Oct 17 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9993 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	10/17/16 → 10/17/16

Keywords

4D-CT
High-dimensional data regression
Respiratory motion estimation
Statistical model

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-47118-1_17

Cite this

Xue, Z., Pino, R., & Teh, B. (2016). Estimating lung respiratory motion using combined global and local statistical models. In P. Coupe, B. C. Munsell, D. Rueckert, Y. Zhan, & G. Wu (Eds.), Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings (pp. 133-140). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9993 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-47118-1_17

Estimating lung respiratory motion using combined global and local statistical models. / Xue, Zhong; Pino, Ramiro ; Teh, Bin.
Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. ed. / Pierrick Coupe; Brent C. Munsell; Daniel Rueckert; Yiqiang Zhan; Guorong Wu. Springer-Verlag, 2016. p. 133-140 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9993 LNCS).

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

Xue, Z, Pino, R & Teh, B 2016, Estimating lung respiratory motion using combined global and local statistical models. in P Coupe, BC Munsell, D Rueckert, Y Zhan & G Wu (eds), Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9993 LNCS, Springer-Verlag, pp. 133-140, 2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016, Athens, Greece, 10/17/16. https://doi.org/10.1007/978-3-319-47118-1_17

Xue Z, Pino R , Teh B. Estimating lung respiratory motion using combined global and local statistical models. In Coupe P, Munsell BC, Rueckert D, Zhan Y, Wu G, editors, Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. Springer-Verlag. 2016. p. 133-140. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-47118-1_17

Xue, Zhong ; Pino, Ramiro ; Teh, Bin. / Estimating lung respiratory motion using combined global and local statistical models. Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. editor / Pierrick Coupe ; Brent C. Munsell ; Daniel Rueckert ; Yiqiang Zhan ; Guorong Wu. Springer-Verlag, 2016. pp. 133-140 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{91bfddcb2cbd426ea04d35f074175712,

title = "Estimating lung respiratory motion using combined global and local statistical models",

abstract = "In image-guided therapy of lung cancer, discrepancies between pre-procedural 3D or 4D-CT and patient{\textquoteright}s current lung shape could reduce the accuracy for guidance. While real-time imaging is often not available, it is necessary to estimate the lung motion from real-time measurable signals such as on-chest/abdominal fiducials or surface. Traditional regression models take the entire lung motion deformation as a whole and perform the estimation in a global manner. Given high dimensionality and complexity of the lung motion patterns, the correlation of lung motion in different local areas with the surface motion could be different. Therefore, we propose a combined global and local statistics-based estimation to improve the estimation accuracy because local deformations have similar patterns and could have higher correlation with the surface motion. Results with 37 4D-CT datasets suggest local motion estimation further improves the performance for lung respiratory motion modeling.",

keywords = "4D-CT, High-dimensional data regression, Respiratory motion estimation, Statistical model",

author = "Zhong Xue and Ramiro Pino and Bin Teh",

note = "Funding Information: This paper was supported by NIH grant 1 R03 EB018977 (ZX). Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016 ; Conference date: 17-10-2016 Through 17-10-2016",

year = "2016",

doi = "10.1007/978-3-319-47118-1_17",

language = "English (US)",

isbn = "9783319471174",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag",

pages = "133--140",

editor = "Pierrick Coupe and Munsell, {Brent C.} and Daniel Rueckert and Yiqiang Zhan and Guorong Wu",

booktitle = "Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings",

}

TY - GEN

T1 - Estimating lung respiratory motion using combined global and local statistical models

AU - Xue, Zhong

AU - Pino, Ramiro

AU - Teh, Bin

N1 - Funding Information: This paper was supported by NIH grant 1 R03 EB018977 (ZX). Publisher Copyright: © Springer International Publishing AG 2016. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2016

Y1 - 2016

N2 - In image-guided therapy of lung cancer, discrepancies between pre-procedural 3D or 4D-CT and patient’s current lung shape could reduce the accuracy for guidance. While real-time imaging is often not available, it is necessary to estimate the lung motion from real-time measurable signals such as on-chest/abdominal fiducials or surface. Traditional regression models take the entire lung motion deformation as a whole and perform the estimation in a global manner. Given high dimensionality and complexity of the lung motion patterns, the correlation of lung motion in different local areas with the surface motion could be different. Therefore, we propose a combined global and local statistics-based estimation to improve the estimation accuracy because local deformations have similar patterns and could have higher correlation with the surface motion. Results with 37 4D-CT datasets suggest local motion estimation further improves the performance for lung respiratory motion modeling.

AB - In image-guided therapy of lung cancer, discrepancies between pre-procedural 3D or 4D-CT and patient’s current lung shape could reduce the accuracy for guidance. While real-time imaging is often not available, it is necessary to estimate the lung motion from real-time measurable signals such as on-chest/abdominal fiducials or surface. Traditional regression models take the entire lung motion deformation as a whole and perform the estimation in a global manner. Given high dimensionality and complexity of the lung motion patterns, the correlation of lung motion in different local areas with the surface motion could be different. Therefore, we propose a combined global and local statistics-based estimation to improve the estimation accuracy because local deformations have similar patterns and could have higher correlation with the surface motion. Results with 37 4D-CT datasets suggest local motion estimation further improves the performance for lung respiratory motion modeling.

KW - 4D-CT

KW - High-dimensional data regression

KW - Respiratory motion estimation

KW - Statistical model

UR - http://www.scopus.com/inward/record.url?scp=84992488516&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84992488516&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-47118-1_17

DO - 10.1007/978-3-319-47118-1_17

M3 - Conference contribution

AN - SCOPUS:84992488516

SN - 9783319471174

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 133

EP - 140

BT - Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings

A2 - Coupe, Pierrick

A2 - Munsell, Brent C.

A2 - Rueckert, Daniel

A2 - Zhan, Yiqiang

A2 - Wu, Guorong

PB - Springer-Verlag

T2 - 2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016

Y2 - 17 October 2016 through 17 October 2016

ER -

Estimating lung respiratory motion using combined global and local statistical models

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this