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What is Learning-Based CBCT Image Correction about?

This study presents a learning-based approach to improve the image quality of Cone Beam CT (CBCT) for clinical applications in radiotherapy by using an alternating random forest (ARF) integrated with an auto-context model. The method involves preprocessing data, extracting informative anatomical features, and iteratively refining the CBCT images, yielding significant improvements in image quality as evidenced by various quantitative metrics. Preliminary results indicate that the proposed algorithm has great

Author

carlos ricardo herrera castillo

Language

EN