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Article

Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis †

School of Computer Science and Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the Wijenayake, U.; Park, S.Y. PCA based analysis of external respiratory motion using an RGB-D camera. In Proceedings of the IEEE International Symposium on Medical Measurements & Applications (MeMeA), Benevento, Italy, 15–18 May 2016.
Sensors 2017, 17(8), 1840; https://doi.org/10.3390/s17081840
Received: 30 May 2017 / Revised: 7 August 2017 / Accepted: 7 August 2017 / Published: 9 August 2017
Accurate tracking and modeling of internal and external respiratory motion in the thoracic and abdominal regions of a human body is a highly discussed topic in external beam radiotherapy treatment. Errors in target/normal tissue delineation and dose calculation and the increment of the healthy tissues being exposed to high radiation doses are some of the unsolicited problems caused due to inaccurate tracking of the respiratory motion. Many related works have been introduced for respiratory motion modeling, but a majority of them highly depend on radiography/fluoroscopy imaging, wearable markers or surgical node implanting techniques. We, in this article, propose a new respiratory motion tracking approach by exploiting the advantages of an RGB-D camera. First, we create a patient-specific respiratory motion model using principal component analysis (PCA) removing the spatial and temporal noise of the input depth data. Then, this model is utilized for real-time external respiratory motion measurement with high accuracy. Additionally, we introduce a marker-based depth frame registration technique to limit the measuring area into an anatomically consistent region that helps to handle the patient movements during the treatment. We achieved a 0.97 correlation comparing to a spirometer and 0.53 mm average error considering a laser line scanning result as the ground truth. As future work, we will use this accurate measurement of external respiratory motion to generate a correlated motion model that describes the movements of internal tumors. View Full-Text
Keywords: respiratory motion; radiotherapy; RGB-D camera; principal component analysis (PCA) respiratory motion; radiotherapy; RGB-D camera; principal component analysis (PCA)
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MDPI and ACS Style

Wijenayake, U.; Park, S.-Y. Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis. Sensors 2017, 17, 1840. https://doi.org/10.3390/s17081840

AMA Style

Wijenayake U, Park S-Y. Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis. Sensors. 2017; 17(8):1840. https://doi.org/10.3390/s17081840

Chicago/Turabian Style

Wijenayake, Udaya, and Soon-Yong Park. 2017. "Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis" Sensors 17, no. 8: 1840. https://doi.org/10.3390/s17081840

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