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Journal of Digital Imaging logoLink to Journal of Digital Imaging
. 1998 Aug;11(3):137–150. doi: 10.1007/BF03168737

Comparison of three tissue composition measurement techniques using digital mammograms — A signal-to-noise study

Darryl S Breitenstein 1,2, Chris C Shaw 1,2,✉
PMCID: PMC3453203  PMID: 9718504

Abstract

Tissue composition measurement may provide a quantitatively and physically meaningful method to objectively determine the “mammographic density” linked to breast cancer risk. A single energy hybrid (SEH) technique is described for measuring the tissue composition on a pixel-by-pixel basis with a single digital mammogram. Theoretical models were derived and used to compute signal-to-noise ratios (SNRs) in tissue composition measurement using the SEH method. The results were compared with those computed for measurements using the dual kVp and dual screen methods. SNRs were theoretically related to the pixel area, total unattenuated detector exposure and fluence spectra of the incident X-rays. SNRs were computed for measurement of the adipose tissue thickness for a 6 cm thick breast, consisting of 50% of adipose tissue and 50% of glandular tissue. Effects of kVp and prepatient filtration were studied by computing the SNRs for various kVps and filters and optimal kVps and filters are determined. The results showed that the SNRs obtained with the SEH method is an order of magnitude better than the dual kVp method, which, in turn, is an order of magnitude better than the dual screen method. When using the optimal kVp’s and no prepatient filtration, the SNRs were computed to be 84.2, 13.2, and 2.0 for the SEH, dual kVp, and dual screen methods, respectively. Prepatient filtration can improve the SNR by as much as 35% for the dual kVp and dual screen techniques with reasonable tube loading factors (8–10).

Key Words: computed radiography (CR), digital mammography, numerical simulation, optimization, tissue composition measurement, signal-to-noise ratio

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Footnotes

This work was supported in part by a grant (CA 51248) from the National Cancer Institute, a grant (DAMD 17-93-J-3009) from the U.S. Army Medical Research and Material Command, a grant (DAAH 04-93-G-0299) from the Department of Defense, and a grant, “Breast Fat Measurement,” from the Pittsburgh Cancer Institute.

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