Image Evaluation and Quality Assurance
Image Evaluation and Quality Assurance
This domain tests systematic image review and the QC tests that keep equipment and practice stable. You must separate technical acceptability from diagnostic adequacy.
Core rules
- Evaluate identification, markers, collimation, exposure, contrast, resolution, distortion, artifacts, and anatomy included. Reject only for a defensible reason; document repeats.
- In digital imaging, use the exposure indicator and deviation index within vendor limits. EI is a receptor exposure index, not patient dose. Window/level can hide under- or overexposure but cannot recover lost data.
- QA includes daily checks where required, detector calibration, flat-field artifacts, monitor checks, and reject analysis. Trend data; a single repeat is a case, a rising rate is a system problem.
- Artifacts have signatures: grid lines from grid error, motion from time, clipping from collimation, and dead pixels from the detector.
Worked example
A chest image has acceptable contrast, but the EI is far above the vendor range. Check AEC field selection, collimation, and patient centering. Reasoning: AEC may have used the wrong chamber; correction is technique or AEC setup, not windowing alone.
Traps
Do not equate high EI with good quality. Do not use window/level to fix exposure. Do not reject images without a documented reason. Do not assume one artifact means detector failure.
Key takeaways
Use criteria, trends, and documented action.