[Research on the Automatic Detection Method of Spatial Resolution for CT Systems Based on the Catphan500 Phantom].
Authors
Affiliations (1)
Affiliations (1)
- Department of Medical Equipment, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233.
Abstract
To explore an automated, rapid, and objective method for measuring CT device spatial resolution based on the Catphan500 phantom. Prospectively collected quality control images from CT devices of different models across various hospitals, trained them using the YOLOv8 deep learning model, and compared the results with manual measurement methods to validate reliability. The YOLOv8 deep learning model achieved a recall of 98%, a precision of 96%, and an [email protected] of 0.988 on the test set, demonstrating excellent performance. The automated detection method measured a single image in an average of only 0.128 seconds, significantly outperforming manual measurement, with only slight differences observed at the extreme resolution. The YOLOv8 deep learning model provides a highly accurate and stable method for automated detection of CT spatial resolution based on the Catphan500 phantom, reducing manual intervention and measurement errors, and offering reliable technical support for the standardization and automation of CT device quality control.