Learning‐based CBCT correction using alternating random forest based on auto‐context model. Issue 2 (11th December 2018)

• Record Type: Journal Article
• Authors: Lei, Yang; Tang, Xiangyang; Higgins, Kristin; Lin, Jolinta; Jeong, Jiwoong; Liu, Tian; Dhabaan, Anees; Wang, Tonghe; Dong, Xue; Press, Robert; Curran, Walter J.; Yang, Xiaofeng
• Is Part Of: Medical physics. Volume 46:Issue 2(2019)
• Page Start: 601
• Page End: 618
• Publication Date: 2018-12-11
• Subjects: adaptive radiotherapy -- alternating random forest -- CBCT correction -- feature selection; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13295
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image‐guided radiotherapy because it provides a foundation for advanced image‐guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currently limited only to patient setup in the clinic because of the severe issues in its image quality. In this study, we develop a learning‐based approach to improve CBCT's image quality for extended clinical applications. Materials and methods: An auto‐context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high‐image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alternating RF is applied to create a CCBCT patch as the output. Moreover, an iterative refinement strategy is exercised to enhance the image quality of CCBCT. Then, all the CCBCT patches are integrated to reconstruct final CCBCT images. Results: The learning‐based CBCT correction algorithm was evaluated using the leave‐one‐out cross‐validation method applied on a cohort of 12 patients' brain data and 14 patients' pelvis data. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), normalized cross‐correlation (NCC) indexes, and spatial nonuniformity (SNU) in the selected regions of interest (ROIs) were used to quantify the proposed algorithm's correction accuracy and generat the following results: mean MAE = 12.81 ± 2.04 and 19.94 ± 5.44 HU, mean PSNR = 40.22 ± 3.70 and 31.31 ± 2.85 dB, mean NCC = 0.98 ± 0.02 and 0.95 ± 0.01, and SNU = 2.07 ± 3.36% and 2.07 ± 3.36% for brain and pelvis data. Conclusion: Preliminary results demonstrated that the novel learning‐based correction method can significantly improve CBCT image quality. Hence, the proposed algorithm is of great potential in improving CBCT's image quality to support its clinical utility in CBCT‐guided adaptive radiotherapy. Abstract :