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Proceedings Paper

Deformable registration of CT and cone-beam CT by local CBCT intensity correction
Author(s): Seyoun Park; William Plishker; Raj Shekhar; Harry Quon; John Wong; Junghoon Lee
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Paper Abstract

In this paper, we propose a method to accurately register CT to cone-beam CT (CBCT) by iteratively correcting local CBCT intensity. CBCT is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. To address this issue, we correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. This correction-registration step is repeated until the result image converges. We tested the proposed method on eight head-and-neck cancer cases and compared its performance with state-of-the-art registration methods, Bspline, demons, and optical flow, which are widely used for CT-CBCT registration. Normalized mutual-information (NMI), normalized cross-correlation (NCC), and structural similarity (SSIM) were computed as similarity measures for the performance evaluation. Our method produced overall NMI of 0.59, NCC of 0.96, and SSIM of 0.93, outperforming existing methods by 3.6%, 2.4%, and 2.8% in terms of NMI, NCC, and SSIM scores, respectively. Experimental results show that our method is more consistent and roust than existing algorithms, and also computationally efficient with faster convergence.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941333 (20 March 2015); doi: 10.1117/12.2082485
Show Author Affiliations
Seyoun Park, John Hopkins Univ. (United States)
William Plishker, IGI Technologies Inc. (United States)
Raj Shekhar, IGI Technologies Inc. (United States)
Children's National Health System (United States)
Harry Quon, Johns Hopkins Univ. (United States)
John Wong, Johns Hopkins Univ. (United States)
Junghoon Lee, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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