research-article

Image-based Respiratory Signal Extraction Using Dimensionality Reduction for Phase Sorting in Cone-Beam CT Projections

Authors:

Geoffrey HugoAuthors Info & Claims

ICCBB '17: Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics

Pages 79 - 84

https://doi.org/10.1145/3155077.3155093

Published: 18 October 2017 Publication History

Abstract

In this paper, a method that detects an image-based respiratory signal automatically in Cone Beam Computed Tomography (CBCT) projection datasets is proposed. The proposed Intensity Flow Dimensionality Reduction method (IFDR) uses optical flow tracking to estimate a set of dense intensity flow vectors from every adjacent pair of projections in the projection dataset. A dimensionality reduction method is applied to the intensity flow vectors to distil them into an eigensystem in which the first few principal components (up to 3 in this work) are combined to represent the motion patterns in the dataset. The algorithm was experimentally evaluated on clinical patient datasets. The extracted respiratory signal using IFDR was compared to respiratory signals measured using 1) the diaphragm position and 2) a trajectory of fiducial markers implanted in and near the tumor. IFDR-based respiratory signal showed an average phase shift of 3.8 ± 1.9 projections (0.35% of the projection set) comparing to the diaphragm position-based signal, and an average phase shift of 3.59 ± 2.44 projections (0.15% of the projection set) comparing to the internal markers-based signal. IFDR was able to extract the respiratory signal in all projections of all the patients' dataset without using any external devices, internal markers or requiring any structure such as the diaphragm to be visible in the CBCT projections. This respiratory signal extracted correlates to the tumor position since the motion was estimated from the soft tissues in and around the tumor.

References

[1]

Berbeco, R. I., Nishioka, S., Shirato, H., Chen, G. T. and Jiang, S. B. 2005. Residual motion of lung tumours in gated radiotherapy with external respiratory surrogates, Phys. Med. Biol., vol. 50, no. 16, (Aug. 2005), 3655--3667.

[2]

Berbeco, R. I., Nishioka, S., Shirato, H. and Jiang, S. B. 2006. Residual motion of lung tumors in end-of-inhale respiratory gated radiotherapy based on external surrogates, Med. Phys., vol. 33, no. 11, (2006), 4149--4156.

[3]

Bergner, F., Berkus, T., Oelhafen, M., Kunz, P., Pan, T. and Kachelrieß, M. 2009. Autoadaptive phase-correlated (AAPC) reconstruction for 4D CBCT, Med. Phys., vol. 36, no. 12, (Dec. 2009), 5695--5706.

[4]

Cervino, L. I., Chao, A. K., Sandhu, A. and Jiang, S. B. 2009. The diaphragm as an anatomic surrogate for lung tumor motion, Phys. Med. Biol., vol. 54, no. 11, (Jun. 2009), 3529--3541.

[5]

Dietrich, L., Jetter, S., Tucking, T., Nill, S. and Oelfke, U. 2006. Linac-integrated 4D cone beam CT: first experimental results, Phys. Med. Biol., vol. 51, (May 2006), 2939--2952.

[6]

Gierga, D., Sharp, G., Brewer, J., Betke, M., Willett, C. and Chen, G. T. 2003. Correlation between external and internal markers for abdominal tumors: implications for respiratory gating, Int. J. Radiat. Oncol. Biol. Phys., vol. 57, no. 2 Suppl., (Oct. 2003), S186-S187.

[7]

Hoisak, J. D., Sixel, K. E., Tirona, R., Cheung, P. C. and Pignol, J. P. 2004. Correlation of lung tumor motion with external surrogate indicators of respiration, Int. J. Radiat. Oncol. Biol. Phys., vol. 60, no. 4, (Nov. 2004), 1298--1306.

[8]

8Horn, B. K. and Schunk, B. G. 1981. Determining optical flow, Artificial intelligence, vol. 17, (Aug. 1981), 185--203.

Digital Library

[9]

9Ionascu, D., Jiang, S. B., Nishioka, S., Shirato, H. and Berbeco, R. I. 2007. Internal-external correlation investigations of respiratory induced motion of lung tumors, Med. Phys, vol. 34, no. 10, (Oct. 2007), 3893--3903.

[10]

10Kavanagh, A., Evans, P. M., Hansen, V. N. and Webb, S. 2009. Obtaining breathing patterns from any sequential thoracic x-ray image set, Phys. Med. Biol., vol. 54, no. 16, (Aug. 2009), 4879--4888.

[11]

Korreman, S., Mostafavi, H., Le, Q. T. and Boyer, A. 2006. Comparison of respiratory surrogates for gated lung radiotherapy without internal fiducials, Acta. Oncol., vol. 45, no. 7, (Sep. 2006), 935--942.

[12]

Li, T., Xing, L., Munro, P., McGuinness, C., Chao, M., Yang, Y., Loo, B. and Koong, A. 2006. Four-dimensional cone-beam computed tomography using an on-board imager, Med. Phys., vol. 33, no. 10, (Sep. 2006), 3825--3833.

[13]

Liu, H. H., Koch, N., Starkschall, G., Jacobson, M., Forster, K., Liao, Z., Komaki, R. and Stevens, C. W. 2004. Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI: Part II-margin reduction of internal target volume, Int. J. Radiat. Oncol. Biol. Phys., vol. 60, no. 5, (Dec. 2004), 1473--1483.

[14]

Marchant, T. E., Amer, A. M. and Moore, C. J. 2008. Measurement of inter and intra fraction organ motion in radiotherapy using cone beam CT projection images, Phys. Med. Biol., vol. 53, (Feb. 2008), 1087--1098.

[15]

Ozhasoglu, C. and Murphy, M. J. 2002. Issues in respiratory motion compensation during external-beam radiotherapy, Int. J. Radiat. Oncol. Biol. Phys., vol. 52, no. 5, (Apr. 2002), 1389--1399.

[16]

Poulsen, P. R., Cho, B. and Keall, P. J. 2008. A method to estimate mean position, motion magnitude, motion correlation, and trajectory of a tumor from cone-beam CT projections for image-guided radiotherapy, Int. J. Radiat. Oncol. Biol. Phys., vol. 72, (Dec. 2008), 1587--1596.

[17]

Rit, S., Sarrut, D. and Ginestet, C. 2005. Respiratory signal extraction for 4D CT imaging of the thorax from conebeam CT projections, Med. Image Comput. Comput. Assist. Interv., vol. 8, no. 1, (2005), 556--563.

Digital Library

[18]

Savitzky, A. and Golay, M. J. E. 1964. Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Analytical Chemistry, vol. 36, (1964), 1627--1639.

[19]

Siochi, R. A. C. 2009. Deriving motion from megavoltage localization cone beam computed tomography scans, Phys. Med. Biol., vol. 54, no. 13, (Jun. 2009), 4195--4212.

[20]

Sonke, J., Zijp, L., Remeijer, P. and van Herk, M. 2005. Respiratory correlated cone beam CT, Med. Phys., vol. 32, no. 4, 2005. 1176--1186.

[21]

Sun, D., Roth, S. and Black, M. J. 2010. Secrets of optical flow estimation and their principles, in Proc. Conf. Computer Vision and Pattern Recognition, 2010.

[22]

Tsunashima, Y., Sakae, T., Shioyama, Y., Kagei, K., Terunuma, T., Nohtomi, A. and Akine, Y. 2004. Correlation between the respiratory waveform measured using a respiratory sensor and 3D tumor motion in gated radiotherapy, Int. J. Radiat. Oncol. Biol. Phys, vol. 60, no. 3, (Nov. 2004), 951--958.

[23]

Vedam, S. S., Kini, V. R., Keall, P. J., Ramakrishnan, V., Mostafavi, H. and Mohan, R. 2003. Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker," Med. Phys., vol. 30, no. 4, (Mar 2003), 505--513.

[24]

Vergalasova, I. Cai, J. and Yin, F. -F. 2012. A novel technique for markerless, self-sorted 4D-CBCT: Feasibility study, Med. Phys., vol. 39, no. 3, (Mar. 2012), 1442--1451.

[25]

Wachinger, C., Yigitsoy, M., Rijkhorst, E. -J. and Navab, N. 2012. Manifold learning for image-based breathing gating in Ultrasound and MRI, Medical Image Analysis, vol. 16, no. 4, (May 2012), 806--818.

[26]

Yan, H., Yin, F. F., Zhu, G. P., Ajlouni, M. and Kim, J. H. The correlation evaluation of a tumor tracking system using multiple external markers, Med. Phys., vol. 33, no. 11, (Oct. 2006), 4073--4084.

[27]

Zijp, L., Sonke, J. -J. and Herk, M. 2004. Extraction of the respiratory signal from sequential thorax cone-beam x-ray images, in The 14th International Conference on the Use of Computers in Radiation Therapy (ICCR), Seoul, Korea, 2004.

Cited By

Sabah SDhou S(2025)Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT ProjectionsApplied System Innovation10.3390/asi80100208:1(20)Online publication date: 26-Jan-2025
https://doi.org/10.3390/asi8010020
Ramesh JSankalpa DMitra RDhou S(2025)Machine Learning-Based X-Ray Projection Interpolation for Improved 4D-CBCT ReconstructionIEEE Open Journal of Engineering in Medicine and Biology10.1109/OJEMB.2024.34596226(61-67)Online publication date: 2025
https://doi.org/10.1109/OJEMB.2024.3459622
Dhou SAlkhodari MIonascu DWilliams CLewis J(2022)Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility StudyJournal of Imaging10.3390/jimaging80200178:2(17)Online publication date: 18-Jan-2022
https://doi.org/10.3390/jimaging8020017
Show More Cited By

Index Terms

Image-based Respiratory Signal Extraction Using Dimensionality Reduction for Phase Sorting in Cone-Beam CT Projections
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

Recommendations

Respiratory signal extraction for 4d CT imaging of the thorax from cone-beam CT projections
MICCAI'05: Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Current methods of four-dimensional (4D) CT imaging of the thorax synchronise the acquisition with a respiratory signal to restrospectively sort acquired data. The quality of the 4D images relies on an accurate description of the position of the thorax ...
An approach for detecting blood vessel diseases from cone-beam CT image
ICIP '95: Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2

This paper is aimed at assisting a medical doctor to detect blood vessel diseases from the high-resolution 3D blood vessels images obtained by cone-beam CT. The important objective is to show how the representation of blood vessels morphology can lead ...
Online 4-D CT estimation for patient-specific respiratory motion based on real-time breathing signals
MICCAI'10: Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III

In image-guided lung intervention, the electromagnetic (EM) tracked needle can be visualized in a pre-procedural CT by registering the EM tracking and the CT coordinate systems. However, there exist discrepancies between the static pre-procedural CT and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICCBB '17: Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics

October 2017

115 pages

ISBN:9781450353229

DOI:10.1145/3155077

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

Univ. of Nebraska at Omaha, USA: University of Nebraska at Omaha, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 October 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCBB 2017

ICCBB 2017: 2017 International Conference on Computational Biology and Bioinformatics

October 18 - 20, 2017

NJ, Newark, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
87
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)2

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Sabah SDhou S(2025)Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT ProjectionsApplied System Innovation10.3390/asi80100208:1(20)Online publication date: 26-Jan-2025
https://doi.org/10.3390/asi8010020
Ramesh JSankalpa DMitra RDhou S(2025)Machine Learning-Based X-Ray Projection Interpolation for Improved 4D-CBCT ReconstructionIEEE Open Journal of Engineering in Medicine and Biology10.1109/OJEMB.2024.34596226(61-67)Online publication date: 2025
https://doi.org/10.1109/OJEMB.2024.3459622
Dhou SAlkhodari MIonascu DWilliams CLewis J(2022)Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility StudyJournal of Imaging10.3390/jimaging80200178:2(17)Online publication date: 18-Jan-2022
https://doi.org/10.3390/jimaging8020017
Dhou SLewis JCai WIonascu DWilliams C(2020)Quantifying day-to-day variations in 4DCBCT-based PCA motion modelsBiomedical Physics & Engineering Express10.1088/2057-1976/ab817e6:3(035020)Online publication date: 9-Apr-2020
https://doi.org/10.1088/2057-1976/ab817e

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten