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Ranking the Relative Importance of Image Quality Features in CT by Consensus Survey
Dustin A Gress1, Ehsan Samei2, Donald P Frush3
1ACR, Reston, Virginia, and Department of Health Administration and Policy, George Mason University, College of Public Health, Fairfax, Virginia; Senior Advisor for Medical Physics, ACR Department of Quality and Safety.
Objective:
This study sought to determine consensus opinions from subspecialty radiologists and imaging physicists on the relative importance of image quality features in CT.
Methods:
A prospective survey of subspecialty radiologists and medical physicists was conducted to collect consensus opinions on the relative importance of 10 image quality features: axial sharpness, blooming, contrast, longitudinal sharpness, low-contrast axial sharpness, metal artifact, motion, noise magnitude, noise texture, and streaking. The survey was first sent to subspecialty radiologists in volunteer leadership roles in the ACR and RSNA, thereafter relying on snowball sampling. Surveyed subspecialties were abdominal, cardiac, emergency, musculoskeletal, neuroradiology, pediatric, and thoracic radiology and medical physics. Individual respondents' ratings were normalized for calculation of mean normalized ratings and priority rankings for each feature within subspecialties. Also calculated were intraclass correlation coefficients across image quality features within subspecialties and analysis of variance across subspecialties within each feature.
Results:
Most subspecialties had moderate to excellent intraclass agreement. For every radiology subspecialty except musculoskeletal, motion was the most important image quality feature. There was agreement across subspecialties that axial sharpness and contrast are only moderately important. There was disagreement across subspecialties on the relative importance of noise magnitude. Blooming was highly important to cardiac radiologists, and noise texture was highly important to musculoskeletal radiologists.
Conclusion:
Image quality preferences differ based on clinical tasks and challenges in each anatomical radiology subspecialty. CT image analysis and development of quantitative measures of quality and protocol optimization-and related policy initiatives-should be specific to radiology subspecialty.
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