We present diffuse optical mammography images that capitalize on the large optical absorption contrast (two orders of magnitude) between blood vessels and breast tissue, thus displaying breast vasculature. We have found a good correspondence between blood vessels displayed in the optical mammogram and those visible in the x-ray mammogram from the same subject in the same view (craniocaudal). By using broadband spectral information (wavelength range: 650-900 nm), we measured the hemoglobin saturation for the blood vessels displayed in the optical mammogram, for which we obtained an average value of 70%. In order to assess the z-axis depth of optical inhomogneities detected in this planar scanning approach, we have introduced pairs of detector optical fibers that are off-axis with respect to the illumination optical fiber. The spatial shift between the locations of the optical inhomogeneities in the two off-axis images can be translated into a depth measurement through a quasi-universal depth-shift reference curve. We report Monte Carlo simulations that show how this depth-shift reference curve is affected by the distance between the source and detector scanning planes (sample thickness) and to the specific arrangement of the two off-axis detectors. Liquid tissue-like phantoms were used to test this depth assessment approach for an absorbing rod placed at a depth of 33.6 mm. The depth measured with our method over the rod length ranged within 33-35 mm, in excellent agreement with the actual rod depth. The ability to identify blood vessels, measure their hemoglobin saturation, and assess their depth within breast tissue is a valuable feature that can advance optical mammography by providing additional structural and functional information about breast tissue.

Date Published: 25 March 2013
PDF: 8 pages
Proc. SPIE 8578, Optical Tomography and Spectroscopy of Tissue X, 85780M (25 March 2013); doi: 10.1117/12.2003946

Published in SPIE Proceedings Vol. 8578:
Optical Tomography and Spectroscopy of Tissue X
Bruce J. Tromberg; Arjun G. Yodh; Eva Marie Sevick-Muraca, Editor(s)