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Proceedings Paper

Accurate tracking of tumor volume change during radiotherapy by CT-CBCT registration with intensity correction
Author(s): Seyoun Park; Adam Robinson; Harry Quon; Ana P. Kiess; Colette Shen; John Wong; William Plishker; Raj Shekhar; Junghoon Lee
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Paper Abstract

In this paper, we propose a CT-CBCT registration method to accurately predict the tumor volume change based on daily cone-beam CTs (CBCTs) during radiotherapy. CBCT is commonly used to reduce patient setup error during radiotherapy, but its poor image quality impedes accurate monitoring of anatomical changes. Although physician’s contours drawn on the planning CT can be automatically propagated to daily CBCTs by deformable image registration (DIR), artifacts in CBCT often cause undesirable errors. To improve the accuracy of the registration-based segmentation, we developed a DIR method that iteratively corrects CBCT intensities by local histogram matching. Three popular DIR algorithms (B-spline, demons, and optical flow) with the intensity correction were implemented on a graphics processing unit for efficient computation. We evaluated their performances on six head and neck (HN) cancer cases. For each case, four trained scientists manually contoured the nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial image registration software based on conventional mutual information (MI), VelocityAI (Varian Medical Systems Inc.). The volume differences (mean±std in cc) between the average of the manual segmentations and automatic segmentations are 3.70±2.30 (B-spline), 1.25±1.78 (demons), 0.93±1.14 (optical flow), and 4.39±3.86 (VelocityAI). The proposed method significantly reduced the estimation error by 9% (B-spline), 38% (demons), and 51% (optical flow) over the results using VelocityAI. Although demonstrated only on HN nodal GTVs, the results imply that the proposed method can produce improved segmentation of other critical structures over conventional methods.

Paper Details

Date Published: 18 March 2016
PDF: 7 pages
Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97860P (18 March 2016); doi: 10.1117/12.2217047
Show Author Affiliations
Seyoun Park, Johns Hopkins Univ. (United States)
Adam Robinson, Johns Hopkins Univ. (United States)
Harry Quon, Johns Hopkins Univ. (United States)
Ana P. Kiess, Johns Hopkins Univ. (United States)
Colette Shen, Johns Hopkins Univ. (United States)
John Wong, Johns Hopkins Univ. (United States)
William Plishker, IGI Technologies (United States)
Raj Shekhar, IGI Technologies (United States)
Children's National Health System (United States)
Junghoon Lee, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 9786:
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Ziv R. Yaniv, Editor(s)

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