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Proceedings Paper

Improving image quality of cone-beam CT using alternating regression forest
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Paper Abstract

We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The welltrained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients’ data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.

Paper Details

Date Published: 9 March 2018
PDF: 7 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057345 (9 March 2018); doi: 10.1117/12.2292886
Show Author Affiliations
Yang Lei, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Xiangyang Tang, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Kristin Higgins, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Anees Dhabaan, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Hyunsuk Shim, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)
Winship Cancer Institute, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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