Abstract
The accelerating advancement in surface-imaging technology has led to promising possibilities with respect to monitoring patient positioning during radiation therapy without the use of radiographic imaging. The aim of this chapter is to introduce theoretical aspects and key computational techniques utilized in estimating positioning errors and analysing the patient’s surface during radiation treatment. In particular, we provide an overview of current surface-imaging technologies. Next, we introduce quantitative approaches for mathematical reconstruction of a patient’s surface using non-uniform rational B-spline (NURBS) modelling and subsequently characterizing of the local shapes of the patient’s surface based on differential geometry. In addition, an iterative closest point (ICP) registration algorithm, which is a basic technique for estimating positioning errors, is explained. We hope that the topics covered in this chapter will be assistive in understanding the current applications in the field and will create launching points for the development of novel solutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agam G, Tang X (2005) A sampling framework for accurate curvature estimation in discrete surfaces. IEEE Trans Vis Comput Graph 11(5):573–583
Bauer S, Seitel S, Hoffman H et al (2013) Real-time range imaging in health care: a survey. In: Time-of-Flight and depth imaging. Sensors, algorithms, and applications, LNCS, vol 8200, pp 228–254
Bergström P (2011) Computational methods for shape verification of free form surfaces. Doctoral Thesis, Luleå University of Technology, Sweden
Bert C, Metheaney KG, Doppke K et al (2005) A phantom evaluation of a stereo-vision surface imaging system for radiotherapy patient setup. Med Phys 32(9):2753–2762
Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. Pattern Anal Mach 14(2):239–256
Brahme A, Nyman P, Skatt B (2008) 4D laser camera for accurate patient positioning, collision avoidance, image fusion and adaptive approaches during diagnostic and therapeutic procedures. Med Phys 35(5):1670–1681. doi:10.1118/1.2889720
Büttgen B, Seitz P (2008) Robust optical time-of-flight range imaging based on smart pixel structures. IEEE Trans Circuits Syst Regul Pap 55(6):1512–1525. doi:10.1109/TCSI.2008.916679
Colombo A, Cusano C, Schettini R (2006) 3D face detection using curvature analysis. Pattern Recogn 39(3):444–455. doi: 10.1016/j.patcog.2005.09.009
Cox MG (1972) The numerical evaluation of B-splines. IMA J Appl Math 10(2):134–149. doi:10.1093/imamat/10.2.134
de Boor C (1972) On calculation with B-splines. J Approx Theory 6:50–62
Fitzpatrick J, West J, Maurer C Jr (1998) Predicting error in rigid-body point based registration. IEEE Trans Med Imaging 17:694–702
Koenderink JJ, van Doorn AJ (1992) Surface shape and curvature scales. Image Vis Comput 10(8):557–565. doi:10.1016/0262-8856(92)90076-F
Meeks SL, Tomé WA, Willoughby TR et al (2005) Optically guided patient positioning techniques. Semin Radiat Oncol 15:192–201. doi:10.1016/j.semradonc.2005.01.004
Pallotta S, Simontacchi G, Marrazzo L et al (2013) Accuracy of a 3D laser/camera surface imaging system for setup verification of the pelvic and thoracic regions in radiotherapy treatments. Med Phys 40(1):011710-1–011710-8
Piegl L, Tiller W (1997) The NURBS book, 2nd edn. Springer, Berlin/Heidelberg
Placht S, Stancanello J, Schaller C et al (2012) Fast time-of-flight camera based surface registration for radiotherapy patient positioning. Med Phys 39(1):4–17
Pressley A (2010) Elementary differential geometry, 2nd edn. Springer, London
Soufi M, Arimura H, Nakamura K et al (2016) Feasibility of differential geometry-based features in detection of anatomical feature points on patient surfaces in range image-guided radiation therapy. Int J Comput Assist Radiol Surg 11:1993. doi:10.1007/s11548-016-1436-x
Timmerman RD, Xing L (2009) Image-guided and adaptive radiation therapy. Wolters Kluwer, London
Wagner TH, Meeks SL, Bova FJ et al (2007) Optical tracking technology in stereotactic radiation therapy. Med Dosim 32:111–120. doi:10.1016/j.meddos.2007.01.008
Wang LT, Solberg TD, Medin PM et al (2001) Infrared patient positioning for stereotactic radiosurgery of extracranial tumors. Comput Biol Med 31(2):101–111. doi:10.1016/S0010-4825(00)00026-3
Willoughby T, Lehmann J, Bencomo J et al (2012) Quality assurance for nonradiographic radiotherapy localization and positioning systems: report of Task Group 147. Med Phys 39(4):1728–1747. doi:10.1118/1.3681967
Yoshitake T, Nakamura K, Shioyama Y et al (2008) Breath-hold monitoring and visual feedback for radiotherapy using a charge-coupled device camera and a head-mounted display: system development and feasibility. Radiat Med 26:50–55. doi:10.1007/s11604-007-0189-4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Soufi, M., Arimura, H. (2017). Surface-Imaging-Based Patient Positioning in Radiation Therapy. In: Arimura, H. (eds) Image-Based Computer-Assisted Radiation Therapy. Springer, Singapore. https://doi.org/10.1007/978-981-10-2945-5_10
Download citation
DOI: https://doi.org/10.1007/978-981-10-2945-5_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2943-1
Online ISBN: 978-981-10-2945-5
eBook Packages: MedicineMedicine (R0)