Spectroscopic x-ray detectors are under development in academic and industry laboratories, and have been receiving attention for their ability to perform single-shot, multi-material decomposition. However, a number of physical processes, including charge sharing and characteristic emission, cause spectral distortion which results in image degradation. In this study, the effects of the system response of cadmium telluride x-ray detectors on basis-material decomposition were investigated. A spatio-energetic model of the system response of spectroscopic x-ray detectors was developed and incorporated into material decomposition of simulated flat-field images. The spatio-energetic decomposition was compared against two other decomposition methods: one which incorporated the energy response and one which assumed an ideal system response. The results were also compared against the decomposition of images simulated assuming ideal conditions (i.e. no charge sharing or characteristic emission). All decomposition methods investigated here were shown to preserve linearity of the iodine signal with respect to the background. However, inclusion of the non-ideal system response resulted in a 3-fold reduction in SNR. Investigating the effects of the spatio-energetic system response on the spatial resolution of basis-material images will be a focus of future work.
KEYWORDS: Breast, Monte Carlo methods, Tissues, X-rays, Systems modeling, Imaging systems, Digital mammography, X-ray detectors, Sensors, Computer simulations
Lesion detectability in digital mammography (DM) is limited by spatial variations in breast tissue composition, commonly referred to as anatomic noise. Quantification of anatomic noise and subsequent incorporation into task-based assessments of DM image quality currently requires an empirical approach, in which the anatomic noise power spectrum (NPS) is extracted from clinical images or images of physical phantoms. This limitation precludes fully theoretical modeling of novel approaches for suppressing anatomic noise. We show theoretically that the anatomic NPS in DM is linearly related to the NPS of the thickness of fibroglandular tissue. We validated this relationship using a validated digital model of a three-dimensional structured breast. We simulated breasts with power-law exponents of 3, thicknesses ranging from 5 cm to 7 cm, and fibgrolandular tissue fractions ranging from 40 % to 60 %. The fibroglandular component of each simulated breast was projected onto a theoretical image plane. For each set of parameters, the fibroglandular NPS was extracted from the ensemble average of 100 fibroglandular projections and fit to a power-law model. The magnitude and power-law exponent of the fibroglandular NPS were then used to predict the system-dependent anatomic NPS over a wide range of tube voltages. Theoretical predictions were then compared with the anatomic NPS extracted from ensembles of simulated x-ray projection images. In all cases, good agreement was observed between the predictions of the linear theory and the simulated anatomic NPS. The linear systems approach developed here can therefore be used to theoretically optimize and evaluate novel breast-imaging techniques without the requirement for empirical input parameters.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.