9 March 2018 A linear systems approach to modeling anatomic noise in digital mammography
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Abstract
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.
Conference Presentation
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Robert Lalonde, Robert Lalonde, Jesse Tanguay, Jesse Tanguay, } "A linear systems approach to modeling anatomic noise in digital mammography", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057303 (9 March 2018); doi: 10.1117/12.2293426; https://doi.org/10.1117/12.2293426
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