Maximum-likelihood estimation of glandular fraction for mammography and its effect on microcalcification detection

Breast tissue is mainly a mixture of adipose and fibro-glandular tissue. Cancer risk and risk of undetected breast cancer increases with the amount of glandular tissue in the breast. Therefore, radiologists must report the total volume glandular fraction or a BI-RADS classification in screening and diagnostic mammography. In this work, a Maximum Likelihood algorithm accounting for count statistics and scatter is shown to estimate the pixel-wise glandular fraction from mammographic images. The pixel-wise glandular fraction provides information that helps localize dense tissue. The total volume glandular fraction can be calculated from pixel-wise glandular fraction. The algorithm was implemented for images acquired with an anti-scatter grid, and those without using the anti-scatter grid but followed by software scatter removal. The work also studied if presenting the pixel-wise glandular fraction image alongside the usual mammographic image has the potential to improve the contrast-to-noise ratio on micro-calcifications in the breast. The algorithms are implemented and evaluated with TOPAS Geant4 generated images with known glandular fractions. These images are also taken with and without microcalcifications present to study the effects of glandular fraction estimation on microcalcification detection. The algorithm was then applied to clinical images with and without microcalcifications. For the TOPAS simulated images, the glandular fraction was estimated with a root mean squared error of 6.6% for the with anti-scatter-grid cases and 7.6% for the software scatter removal (no anti-scatter grid) cases for a range of 2-9 cm compressed breast thickness. Average absolute errors were 4.5% and 4.7% for a range of 2-9 cm compressed breast thickness respectively for the anti-scatter grid and software scatter-removal methods. For higher thickness and glandular fraction, the errors were higher. For the extreme case of 9 cm thickness, the glandular fraction estimation yielded 5%, 13% and 16% mean absolute errors for 20%, 30% and 50% glandular fraction. These errors lowered to 1.5%, 9% and 13.2% for a narrower spectrum for the 9 cm. Results from clinical images (where the true glandular fraction is unknown) show that the algorithm gives a glandular fraction within the average range expected from the literature. For microcalcification detection, the contrast-to-noise ratio improved by 17.5-548% in clinical images and 5.1-88% in TOPAS images. A method for accurately estimating the pixel-wise glandular fraction in images, which provides localization information about breast density, was demonstrated. The glandular fraction images also showed an improvement in contrast to noise ratio for detecting microcalcifications, a risk factor in breast cancer.