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Published on: September 11, 2011
Improving the Safety of Computed Tomography Through Automated Quality Measurement: A Radiologist Reader Study of
Rebecca Smith-Bindman1, Yifei Wang, Carly Stewart
1From the Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA (R.S.-B., Y.W., C.S., J.L., P.W.C.); Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, CA (R.S.-B.); Philip R Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, CA (R.S.-B.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA (M.K.); Department of Radiology, University of Washington, Seattle, WA (A.C.W.); Department of Radiology, University of Maryland Medical Center and Baltimore VA Medical Center, Baltimore, MD (E.S.); Department of Medicine and Pediatrics, University of California Davis Health, Sacramento, CA (M.R., P.S.R.); Department of Radiology, University of Wisconsin, Madison, WI (T.P.S.); and Kaiser Foundation Health Plan and Hospitals (A.B.B.).
Objectives:
The Centers for Medicare and Medicaid Services funded the development of a computed tomography (CT) quality measure for use in pay-for-performance programs, which balances automated assessments of radiation dose with image quality to incentivize dose reduction without compromising the diagnostic utility of the tests. However, no existing quantitative method for assessing CT image quality has been validated against radiologists' image quality assessments on a large number of CT examinations. Thus to develop an automated measure of image quality, we tested the relationship between radiologists' subjective ratings of image quality with measurements of radiation dose and image noise.
Materials And Methods:
Board-certified, posttraining, clinically active radiologists rated the image quality of 200 diagnostic CT examinations from a set of 734, representing 14 CT categories. Examinations with significant distractions, motion, or artifact were excluded. Radiologists rated diagnostic image quality as excellent, adequate, marginally acceptable, or poor; the latter 2 were considered unacceptable for rendering diagnoses. We quantified the relationship between ratings and image noise and radiation dose, by category, by analyzing the odds of an acceptable rating per standard deviation (SD) increase in noise or geometric SD (gSD) in dose.
Results:
One hundred twenty-five radiologists contributed 24,800 ratings. Most (89%) were acceptable. The odds of an examination being rated acceptable statistically significantly increased per gSD increase in dose and decreased per SD increase in noise for most categories, including routine dose head, chest, and abdomen-pelvis, which together comprise 60% of examinations performed in routine practice. For routine dose abdomen-pelvis, the most common category, each gSD increase in dose raised the odds of an acceptable rating (2.33; 95% confidence interval, 1.98-3.24), whereas each SD increase in noise decreased the odds (0.90; 0.79-0.99). For only 2 CT categories, high-dose head and neck/cervical spine, neither dose nor noise was associated with ratings.
Conclusions:
Radiation dose and image noise correlate with radiologists' image quality assessments for most CT categories, making them suitable as automated metrics in quality programs incentivizing reduction of excessive radiation doses.
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