KEYWORDS: Breast, Signal to noise ratio, Image quality, Modulation transfer functions, Digital mammography, X-rays, Spatial frequencies, Breast cancer, Mammography, Polymethylmethacrylate
Breast density has a close relationship with breast cancer risk. The exposure parameters must be appropriately chosen for
each breast. However, the optimal exposure conditions for digital mammography are uncertain in clinical. The exposure
parameters in digital mammography must be optimized with maximization of image quality and minimization of
radiation dose. We evaluated image quality under different exposure conditions to investigate the most advantageous
tube voltage. For different compressed breast phantom thicknesses and compositions, we measured the Wiener spectrum
(WS), noise-equivalent number of quanta (NEQ), and detective quantum efficiency (DQE). In this study, the
signal-to-noise ratios were derived from a perceived statistical decision theory model with the internal noise of eye-brain
system (SNRi), contrived and studied by Loo et al.1 and Ishida et al.2 These were calculated under a fixed average
glandular dose. The WS values were obtained with a fixed image contrast. For 4-cm-thick and 50% glandular breast
phantoms, the NEQ showed that high voltages gave a superior noise property of images, especially for thick breasts, but
the improvement in the NEQ by tube voltage was not so remarkable. On the other hand, the SNRi value with a Mo filter
was larger than that with a Rh filter. The SNRi increased when the tube voltage decreased. The result differed from those
of WS and NEQ. In this study, the SNRi depended on the contrast of signal. Accuracy should be high with an intense,
low-contrast object.
KEYWORDS: Signal to noise ratio, Signal detection, Visualization, Image quality, Modulation transfer functions, Interference (communication), Spatial frequencies, Eye models, Visual process modeling, Image visualization
The effects of imaging parameters on detectability have not yet been clarified. Therefore, we investigated the
usefulness of signal-to-noise ratios (SNRs) considered as human visual characteristics, such as the visual spatial
frequency response and the internal noise in the eye-brain system.
We examined the amplitude model (SNRa), matched filter model (SNRm), and internal noise model (SNRi) to study
the relationship between these SNRs and the visual image quality for signal detection. The test images were simulated by
the superimposition of low-contrast signals on a uniform noisy background. The SNRs were obtained for 15 imaging
cases with various signal sizes, signal contrasts, exposure levels, and number of acrylic plates used as breast phantoms.
The SNRs were calculated by measuring the spatial frequency characteristics of the signal, modulation transfer
function (MTF) of the system, display MTF, and overall Wiener spectrum (WS).
In the perceptual evaluation, we applied the 16-alternative forced choice (16-AFC) method. The signal detectability
was defined as the number of detected signals divided by the total number of signals. We studied the relationship
between SNR and signal detectability using Spearman's rank correlation coefficient.
The correlation coefficient of SNRi was 0.93, making it the highest among the three SNR types. That of SNRm was
0.91; it correlated at the same level as SNRi although it is not considered human visual characteristics. That of SNRa
was 0.45. SNRi, which incorporated the visual characteristics, explained the visual image quality well.
KEYWORDS: Signal to noise ratio, X-rays, Modulation transfer functions, Digital mammography, Interference (communication), Mammography, Image quality, Breast, Spatial frequencies, Visualization
The use of digital mammography systems has become widespread recently. However, the optimal exposure parameters
are uncertain in clinical practice. We need to optimize the exposure parameter in digital mammography while
maximizing image quality and minimizing patient dose. The purpose of this study was to evaluate the most beneficial
exposure variable-tube voltage for each compressed breast
thickness-with these indices: noise power spectrum, noise
equivalent quanta, detective quantum efficiency, and signal-to-noise ratios (SNR). In this study, the SNRs were derived
from the perceived statistical decision theory model with the internal noise of eye-brain system (SNRi), contrived and
studied by Loo LN1), Ishida M et al. 2) These image quality indices were obtained under a fixed average glandular dose
(AGD) and a fixed image contrast. Our results indicated that when the image contrast and AGD was constant, for
phantom thinner than 5 cm, an increase of the tube voltage did not improve the noise property of images very much. The
results also showed that image property with the target/filter Mo/Rh was better than that with Mo/Mo for phantom
thicker than 4 cm. In general, it is said that high tube voltage delivers improved noise property. Our result indicates that
this common theory is not realized with the x-ray energy level for mammography.
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