Translator Disclaimer
9 March 2018 Improving image quality of cone-beam CT using alternating regression forest
Author Affiliations +
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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yang Lei, Xiangyang Tang, Kristin Higgins, Tonghe Wang, Tian Liu, Anees Dhabaan, Hyunsuk Shim, Walter J. Curran, and Xiaofeng Yang "Improving image quality of cone-beam CT using alternating regression forest", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057345 (9 March 2018);

Back to Top