One year's results from a server-based system for performing reject analysis and exposure analysis in computed...

Area of Science:

• Radiologic Technology
• Medical Imaging Quality Assurance

Background:

• Rejected radiographic images lead to increased patient radiation exposure and operational inefficiencies.
• Patient positioning, motion, and digital imaging artifacts contribute to image rejection.
• Current digital radiography systems require robust quality control measures.

Purpose of the Study:

• To implement and evaluate a centralized, automated system for collecting and analyzing rejected image and exposure indicator data.
• To identify the primary causes of image rejection and assess their impact on radiation dose and efficiency.
• To determine the effectiveness of combining reject analysis with exposure indicator analysis for quality assurance.

Main Methods:

• A server-based solution was developed for automated data collection, archival, and distribution.
• Reject Analysis Program (RAP) and exposure indicator data were collected over one year.
• Data were analyzed, sorted by rejection reason, body part, and clinical area, and stratified by computed radiography (CR) usage.

Main Results:

• The institutional monthly reject rate ranged from 8% to 10%, with positioning errors accounting for 77.3% of repetitions.
• Clinical areas with less frequent computed radiography (CR) use exhibited higher reject rates.
• Exposure indicator (S value) distributions were log-normal, skewed, and leptokurtic, with observed decreases during student rotations and CR plate reader calibrations.

Conclusions:

• Reject analysis remains essential for quality assurance in digital imaging, despite technological advancements.
• Integrating reject analysis with exposure indicator analysis is vital for managing radiation exposure in digital radiography.
• Combined analysis provides a powerful tool for comprehensive quality assurance and operational efficiency.