Feasibility study of deep-learning-based bone suppression incorporated with single-energy material decomposition technique in chest X-rays.

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Abstract

To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause of chest-related diagnostic errors.This study presents a deep-learning-based bone suppression method where a residual U-Net model is trained for chest X-rays using dataset generated from the single-energy material decomposition (SEMD) technique on computed tomography (CT). Synthetic projection images and soft-tissue selective images were obtained from the CT dataset via the SEMD, which were then used as the input and label data of the U-Net network. The trained network was tested on synthetic chest X-rays and two real chest radiographs.Bone-suppressed images of the real chest radiographs obtained by the proposed method were similar to the results from the AAPM lung CT data; pulmonary nodules in the soft-tissue selective images appeared more clearly than in the synthetic projection images. The PSNR and SSIM values measured between the output and the corresponding label images were approximately 17.85 and 0.90, respectively.The proposed method effectively yielded bone-suppressed chest X-ray images, indicating its clinical usefulness, and it can improve the detection of lung abnormalities in chest X-rays.The idea of using SEMD to obtain large amounts of paired images for deep-learning-based bone suppression algorithms is novel.