Erratum: Deep learning-based super-resolution method for projection image compression in radiotherapy
Year: 2025
Type: article
Abstract: Background: , thus this technique may be used as an alternative to conventional ACTA. Methods: undergoing CTA examination with a Philips iCT 256-slice scanner were randomly assigned into two groups according to the examination technique used: three-low group (n=40): tube voltage: 100 kV, contrast agent injection rate: 3 mL/s, contrast agent volume: 50 mL; control group (n=40): tube voltage: 120 kV, injection rate: 5 mL/s, contrast agent: 80 mL. Aortic CT value and image noise were detected, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed, and the subjective score of the image quality was evaluated. The effective radiation dose was computed according to a formula. Results: 4.2±0.9) between the two groups (P>0.05). Conclusions: ; the effective radiation dose was decreased by 76.2%, the contrast agent volume was decreased by 37.5%, and all images met the diagnostic requirements.
Authors: Zhijian Chen, Shaogang Wang, Ran Zhang, Ying Chen, Xiaoyi Deng, Xueqin Zhang, Tao Zhang
Institutions: Nantong University, Jiangsu University
Language: English
Topic: Radiation Dose and Imaging
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4414483670