Mammographic image quality assessment by a contrast-to-noise ratio for clinical images

Abstract

Purpose: To test the association between contrast-to-noise ratio (CNR) measurements made on digital mammograms (DM), human reader performance in a lesion detection task using the same images, and image quality (IQ) as predicted by phantom measurements. Methods: DM from 162 women were evaluated for their CNR using a novel metric for application on clinical images. The original unprocessed images were tested (100% dose), as well as the same images after processing to simulate a 50% and 25% relative dose level. IQ measurements from a CDMAM phantom images, as well as human reader calcification cluster detectability ratings on the clinical image set for the three treatments were used to provide ground truth for human lesion detection performance. Analysis was performed to test for association between DM image CNR at the three dose levels, the CDMAM measurements, and reader performance as quantified by a reader-averaged jack-knifed free response operating characteristic (JAFROC) figure of merit (FoM). Results: The clinical image CNR was strongly correlated with the JAFROC FoM and CDMAM threshold gold thicknesses (r=0.98, and r=0.99 @ 0.25 mm, r=0.94 @ 0.1 mm discs, respectively). On a per-image basis, strong associations between CNR and measures of beam quality and exposure were also found that indicate sensitivity to imaging technique factors while remaining independent of signal variations due to breast parenchyma. Conclusions: Using a clinical image CNR it is possible to objectively predict IQ in mammographic images. As such, this metric could provide a means to perform a practical continuous DM system performance monitoring.