The MTF (modulation transfer function) of digital radiography systems can be enhanced in the spatial frequency domain
due to their high signal to noise ratio. A Wiener filter, which requires prior estimation of the noise and signal power
spectrum of the images, was used to compensate MTF of the detector and thereby optimally restore the images details.
We studied the noise characteristics of two flat panel detectors with structured columnar scintillator (CsI) and granular
scintillator (Gd2O2S). A noise model formulating noise transfer process was applied to estimate the noise components for
the filter. Signal model was based on dose of the application. We revisited the noise and signal model that was used in
previous work by Souchay et al. for mammography application , considering the difference in detector characteristics
and the applications (extremity x-ray) that we are specifically investigating. Starting with real clinical images, we used
an observer study method to measure the visually optimal parameter for the Wiener filter. A set of clinical images was
used to evaluate the radiologists' preferences to compensated images against the reference images. Statistical results
from three experienced radiologists ranking results show that the compensated images are preferred over the reference
Proc. SPIE. 7258, Medical Imaging 2009: Physics of Medical Imaging
KEYWORDS: Signal to noise ratio, Point spread functions, Imaging systems, Interference (communication), Linear filtering, Medical imaging, Data acquisition, Reconstruction algorithms, Modulation transfer functions, Anisotropy
In limited angular aperture tomosynthesis systems, the addition of new degrees of freedom for data
acquisition, such as angular aperture and sampling, requires specific optimizations. Typical optimization
criteria include MTF, SNR, and NEQ. However, the strong anisotropy of the sampling frequency on the zaxis
is usually neglected. Considering the signal in slices as the information contained within the volume
defined by the slice plane and the z-sampling interval, the MTF of the reconstruction is obtained by
integrating reconstruction blur within the slice. The relationship between z-sampling and aperture is
proposed in terms of preservation of the DQE.
To accurately detect radiological signs of cancer, mammography requires the best possible image quality for a target patient dose. The application of automatic optimization of parameters (AOP) to digital systems has been improved recently. The metric used to derive this AOP was based on the expected CNR of calcium material in a uniform background. In this work, we use a new metric, based on the detection performance of an a-contrario observer on lesions in simulated images. Breast images at various thicknesses and glandularity levels were simulated with flat and textured backgrounds. Various exposure spectra (Mo/Mo, Mo/Rh and Rh/Rh anode/filter materials, kVp ranging from 25 to 33 kV) were considered. The tube output has been normalized in order to obtain comparable AGD values for each image of a given breast over the various acquisition techniques. Images were scored with the a-contrario observer, the performance criterion being the minimal lesion size needed to reach a given detection threshold. The optimal spectra are found similar to those delivered by the AOP in both flat and textured backgrounds. The choice of the anode/filter combination appears to be more critical than kVp adjustments in particular for the thicker breasts. Our approach also yields an estimate of the detection variability due to texture signal. We found that the anatomical structure variability cannot be overcome by beam quality optimization of the current system in presence of complex background, which confirms the potential benefit of any imaging technology reducing the variability of detection due to texture.
Burgess showed that lesion detectability does have a non-trivial behavior with textured mammographic backgrounds: the threshold detectability occurs when the log contrast is linearly related to the log size with positive slope. Grosjean et al. proposed the a-contrario detector as an acceptable observer for detection on such backgrounds. In this study, we quantitatively simulated projected breast images containing lesions with a variety of sizes and thicknesses, for a 55 mm thick, 50/50 glandular breast and with different textured background types generated by the power-law filtered noise model proposed by Burgess. The acquisition parameters used in the simulation correspond to the optimal techniques provided by a digital mammography system for that specific breast. Images have been automatically scored by the a-contrario detector in order to find the minimum thickness of the lesion needed to reach the detection threshold.
Taking into account the Fourier spectrum properties of the breast texture and using the a-contrario observer as a new metric for the detection task, we found the same detection slopes as described by Burgess. With our quantitative simulation, which includes a realistic image chain of a digital mammography system, and with the implementation of a novel detection process, we found that for the considered lesion sizes, lesions are easier to detect on textures with a high value of power-law exponent.