While previous research has been done to determine the contrast detection threshold in medical images, we have found it
difficult to translate the results into settings that can be used for the optimization of image quality. Since many of these
papers were done before the widespread use of DICOM GSPS calibrated monitors, how the GSPS affects the detection
threshold and whether the median background intensity shift has been minimized by GSPS remain unknown. We set out
to determine if the median background affected the detection of a low-contrast object in a clustered lumpy background,
which simulated a mammography image. Our results show that shifts in the median background intensity did not affect
the detection performance. The contrast detection threshold appears close to +3 gray levels above the background.
In order to optimize image quality, Figures of Merit (FOM) have been developed, including Signal-to-Noise ratio (SNR),
Contrast-to-Noise ratio (CNR), and CNR2-to-Dose ratio (CNR2/PED). Some FOMs are designed to describe the
performance of system components: Detective Quantum Efficiency (DQE) and Noise Equivalent Quanta (NEQ) are
examples. A single FOM has the downside that optimization is inherently driven by the design of the FOM and cannot
be changed. In this paper, we propose using a multi-parametric methodology for optimizing multiple input factors and
multiple response measurements. This methodology has been developed in the statistical community as an offshoot of
MANOVA (Multivariate ANalysis Of VAriance) analysis. In this paper, we acquired 120 images with various
techniques and measured four individual image quality metrics. We then developed multivariate prediction formula for
each metric and determined the global optimum operating point, using desirability functions. We demonstrate the power
of this methodology over single FOM metrics.
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