Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography

نویسندگان

چکیده

Recently, some facilities have utilized the dual-energy subtraction (DES) technique for chest radiography to increase pulmonary lesion detectability. However, availability of is limited certain facilities, in addition other limitations, such as increased noise high-energy images and motion artifacts with one-shot two-shot methods, respectively. The aim this study was develop artificial intelligence-based DES (AI–DES) technology overcome these limitations. Using a trained pix2pix model on clinically acquired radiograph pairs, we successfully converted 130 kV into virtual 60 that closely resemble real images. averaged peak signal-to-noise ratio (PSNR) structural similarity (SSIM) between were 33.8 dB 0.984, We also achieved production soft-tissue- bone-enhanced using weighted image process soft-tissue-enhanced exhibited sufficient bone suppression, particularly within lung fields. Although contained around lower thoracic lumbar spines, superior sharpness characteristics presented. main contribution our development its ability provide selectively enhanced specific tissues only obtained via routine radiography. This suggests potential improve detectability lesions while addressing challenges associated existing technique. further improvements are necessary quality.

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2023

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app13127220