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위의 그림은 중국 선전 (Shenzhen)에서 촬영된 흉부 X-선 영상에 제안한 기술을 적용한 결과입니다. 이중에너지를 사용하지 않았음에도, 제안한 기술을 사용함으로써 연부 조직과 뼈 성분을 구분하여 영상화할 수가 있었습니다. 첫 번째 환자는 Right Lung Apex에 Tuberculosis 가 있는 환자였고, 두 번째 환자는 Right Lung에 Nodule이 있는 환자였습니다. 제안한 기술은 병변의 신호에는 왜곡을 주지 않았고, 효과적으로 뼈 성분을 제거하여 병변 진단에 도움을 줄 수 있을 것으로 기대됩니다.

Abstract
Dual-energy chest radiography (DECR) is a medical imaging technology that can improve diagnostic accuracy. This technique can decompose single-energy chest radiography (SECR) images into separate bone- and soft tissue-only images. This can, however, double the radiation exposure to the patient. To address this limitation, we developed an algorithm for the synthesis of DECR from a SECR through deep learning. To predict high resolution images, we developed a novel deep learning architecture by modifying a conventional U-net to take advantage of the high frequency-dominant information that propagates from the encoding part to the decoding part. In addition, we used the anticorrelated relationship (ACR) of DECR for improving the quality of the predicted images. For training data, 300 pairs of SECR and their corresponding DECR images were used. To test the trained model, 50 DECR images from Yonsei University Severance Hospital and 662 publicly accessible SECRs were used. To evaluate the performance of the proposed method, we compared DECR and predicted images using a structural similarity approach (SSIM). In addition, we quantitatively evaluated image quality calculating the modulation transfer function and coefficient of variation. The proposed model selectively predicted the bone- and soft tissue-only CR images from an SECR image. The strategy for improving the spatial resolution by ACR was effective. Quantitative evaluation showed that the proposed method with ACR showed relatively high SSIM (over 0.85). In addition, predicted images with the proposed ACR model achieved better image quality measures than those of U-net. In conclusion, the proposed method can obtain high-quality bone- and soft tissue-only CR images without the need for additional hardware for double x-ray exposures in clinical practice.

Author information

Lee D1, Kim H, Choi B, Kim HJ.
1
Department of Radiation Convergence Engineering, Research Institute of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, Republic of Korea.

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