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Evaluation of Image Quality in Digital Radiography Based On Marginal Detectability

A Scott*, Y Zhou, D Zhang, N Binesh, C Lee, W Bosteder, Cedars-Sinai Medical Center, Los Angeles, CA

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose
We desired to measure how three different digital detectors performed at higher and lower exposure index (EI) in terms of visual and quantitative metrics, particularly the effect on features at the borderline of detectability. The result affected the institution’s choice of clinical target EI.

Methods
The experiment used two digital detectors (“A” and “B”) from vendor 1 and one from vendor 2. An aluminum contrast-recovery plate, with low-contrast targets in a matrix of diameters and hole depths, was placed directly on the detector/grid with 2” – 8” Lucite above. The manual techniques used a kVp between 55 and 110 as appropriate to the Lucite thickness and a mAs producing an EI of approximately either 220 or 140. Images were acquired for each detector at both EI values for all Lucite thicknesses and scored by seven physicists and technologists in terms of feature visibility for each feature diameter. Contrast-to-noise ratio (CNR) was calculated for each feature using an ROI over the feature and a background annulus of similar area. The uncertainty of the CNR was determined by sampling the background at each feature size, finding residuals from an overall background fit, and then calculating a standard deviation in the noise for each size.

Results
The marginal features for each feature size was determined by the reader score of the last detectable feature. For the CNR metric, the difference between the CNR values of corresponding targets in high and low EI images was significant (p < 0.05) for detector 1A and not significant (p > 0.05) for the marginal features for detectors 1B and 2.

Conclusion
Based on both reader scoring and CNR measurements of phantoms, patient doses can be lowered by 35% for detectors 1B and 2 without a statistically significant difference in lesion perceptibility of the marginally visible feature.

Download ePoster [PDF]

Keywords

Digital Imaging

Taxonomy

IM- X-ray: Digital radiography (DR and CR)

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