Affiliations: Center for Bioengineering and School of Electrical and
Computer Engineering, University of Oklahoma, Norman, OK, USA | Department of Radiology, University of Iowa, Iowa
City, IA, USA
Note: [] Corresponding author: Hong Liu, Center for Bioengineering and
School of Electrical and Computer Engineering, University of Oklahoma, 101
David L Boren Blvd, Norman, OK 73019, USA. Tel.: +1 405 325 4286; Fax: +1 405
325 7066; E-mail: [email protected]
Abstract: BACKGROUND: Accurately depicting breast tumors located posteriorly,
close to the chest wall musculature, with conventional mammography is a
technical challenge. OBJECTIVE: This study demonstrates the proof of concept of an x-ray
fluorescence mapping (XFM) technique to address this issue. METHODS: A tissue-equivalent gel phantom is designed to mimic
structures in the central part of a compressed breast. The posterior aspect of
the breast and adjacent pectoralis major muscle are represented by another
10-mm-thickness breast tissue simulation phantom (BR12) that is attached to the
back of the gel phantom as a region of interest (ROI). Two gold nanoparticle
(GNP) solutions are embedded into the ROI to simulate varying GNP uptake within
breast lesions. The ROI is imaged through performing the XFM technique with an
x-ray pencil-beam and a single spectrometer. RESULTS: A 2D mapping of the middle plane in the ROI demonstrates
feasibility and matches well the known spatial distribution and different GNP
concentrations. 3D reconstruction of the ROI is easily rendered by repeating
the 2D mapping process. CONCLUSION: XFM system geometry and its insensitivity to attenuation
coefficients of breast tissue components are unique characteristics that may
complement conventional mammography and improve the detection of breast cancers
located posteriorly, adjacent to or overlying the chest wall musculature.
Keywords: Breast cancer detection, posteriorly located breast tumors, chest wall musculature, breast-mimicking phantom, x-ray fluorescence mapping (XFM), gold nanoparticles (GNPs)
