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A robust method of identifying the optimal cancellation parameters for dual-energy chest x-ray imaging

This work proposes a robust method of identifying the optimal cancellation parameters for dual-energy imaging. We designed the proposed method in a simple and practical way for dual-energy subtraction by utilizing histogram information rather than spatial information of images. A classification predictive modeling based on XGBoost was employed to identify cancellation parameters for soft-tissue and bone selected images. We verified the robust performance of the proposed method for 500 chest x-ray examinations by comparing predicted cancellation parameters with the optimal values determined by well-trained radiologists. The value of the proposed work may contribute to advancements in chest x-ray imaging technologies.