Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks

作者：Hua Zhong, Qianwen Huang, Xiaoli Zheng, Yong Wang, Yanan Qian, Xingbiao Chen, Jinan Wang, Shaoyin Duan · 发表于：BMC Medical Imaging · 年份：2024 · DOI：10.1186/s12880-024-01331-3 · 被引用次数：10 · 研究领域：Advanced X-ray and CT Imaging、Advanced X-ray Imaging Techniques、Cardiac Imaging and Diagnostics

Abstract Background Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. Purpose To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI 40keV ) of the upper abdomen CT scan. Methods Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI 40keV ) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI 40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. Results The 192nd iteration produced the best generator mode (lower MAE and...