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Learning-based CBCT Correction Using Alternating Random Forest Based on Auto-context Model.

Medical Physics 2018 November 25
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 to patient setup only 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.

METHODS AND MATERIALS: 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 random forest is applied to create a CCBCT patch as the output. Moreover, an iterative refinement strategy is exercised to enhance the image quality of corrected CBCT. 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 non-uniformity (SNU) in the selected regions of interest (ROIs) were used to quantify the proposed algorithm's correction accuracy and generated the following results: mean MAE = 12.81±2.04 HU and 19.94±5.44 HU, mean PSNR = 40.22±3.70 dB 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.

INDEX TERMS: CBCT correction, adaptive radiotherapy, alternating random forest, feature selection. This article is protected by copyright. All rights reserved.

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