Current medical imaging · 2026
The Quality Assessment of Virtual Unenhanced and Blending Images Derived from Dual-Energy CT for Detecting Colorectal Cancer.
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Chen Feixiang, Xu Weize, Zhu Jianfeng, Wang Meirong, Chen Jinghao, Xiao Jing, Yang Jushun, He Bosheng
Аннотация
INTRODUCTION: This study aimed to evaluate the image quality of virtual unenhanced and blending images from dual-energy CT for detecting colorectal cancer (CRC).
MATERIALS AND METHODS: A total of 72 patients with pathologically diagnosed CRC underwent abdominal dual-energy CT, following which virtual unenhanced, linear blending, and non-linear blending images were generated by post-processing reconstruction. Both subjective and objective evaluations were conducted on these images, with signal-to-noise (SNR) and contrast-to-noise ratio (CNR) calculations conducted for organs, such as the liver, pancreas, and spleen.
RESULTS: Virtual unenhanced images of CRC, extraserosal fat of the tumor, liver, pancreas, spleen, kidney, and subcutaneous fat showed a lower signal intensity than both linear and non-linear blending images (P < 0.05), while the CNR of virtual unenhanced images was higher than linear and nonlinear blending images (P < 0.05). Except for CRC lesions, the SNR of other organs in virtual unenhanced images was higher than in linear and non-linear blending images (P < 0.05). There were no significant differences in subjective image scores and the number of conventional lesions between virtual unenhanced image, linear, and non-linear blending (P ≥ 0.05). The Kappa coefficients for evaluating extraserosal invasion were 0.722, 0.584, and 0.584 for virtual unenhanced, linear blending, and non-linear blending images, respectively, with corresponding accuracies of 86.1%, 79.2%, and 79.2%.
CONCLUSION: Virtual unenhanced images of patients with CRC can provide high-quality images for diagnostic evaluation, potentially replacing linear blending and non-linear blending images in plain scans.
MATERIALS AND METHODS: A total of 72 patients with pathologically diagnosed CRC underwent abdominal dual-energy CT, following which virtual unenhanced, linear blending, and non-linear blending images were generated by post-processing reconstruction. Both subjective and objective evaluations were conducted on these images, with signal-to-noise (SNR) and contrast-to-noise ratio (CNR) calculations conducted for organs, such as the liver, pancreas, and spleen.
RESULTS: Virtual unenhanced images of CRC, extraserosal fat of the tumor, liver, pancreas, spleen, kidney, and subcutaneous fat showed a lower signal intensity than both linear and non-linear blending images (P < 0.05), while the CNR of virtual unenhanced images was higher than linear and nonlinear blending images (P < 0.05). Except for CRC lesions, the SNR of other organs in virtual unenhanced images was higher than in linear and non-linear blending images (P < 0.05). There were no significant differences in subjective image scores and the number of conventional lesions between virtual unenhanced image, linear, and non-linear blending (P ≥ 0.05). The Kappa coefficients for evaluating extraserosal invasion were 0.722, 0.584, and 0.584 for virtual unenhanced, linear blending, and non-linear blending images, respectively, with corresponding accuracies of 86.1%, 79.2%, and 79.2%.
CONCLUSION: Virtual unenhanced images of patients with CRC can provide high-quality images for diagnostic evaluation, potentially replacing linear blending and non-linear blending images in plain scans.