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Proceedings Paper

Coded aperture coherent scatter imaging for breast cancer detection: a Monte Carlo evaluation
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Paper Abstract

It is known that conventional x-ray imaging provides a maximum contrast between cancerous and healthy fibroglandular breast tissues of 3% based on their linear x-ray attenuation coefficients at 17.5 keV, whereas coherent scatter signal provides a maximum contrast of 19% based on their differential coherent scatter cross sections. Therefore in order to exploit this potential contrast, we seek to evaluate the performance of a coded- aperture coherent scatter imaging system for breast cancer detection and investigate its accuracy using Monte Carlo simulations. In the simulations we modeled our experimental system, which consists of a raster-scanned pencil beam of x-rays, a bismuth-tin coded aperture mask comprised of a repeating slit pattern with 2-mm periodicity, and a linear-array of 128 detector pixels with 6.5-keV energy resolution. The breast tissue that was scanned comprised a 3-cm sample taken from a patient-based XCAT breast phantom containing a tomosynthesis- based realistic simulated lesion. The differential coherent scatter cross section was reconstructed at each pixel in the image using an iterative reconstruction algorithm. Each pixel in the reconstructed image was then classified as being either air or the type of breast tissue with which its normalized reconstructed differential coherent scatter cross section had the highest correlation coefficient. Comparison of the final tissue classification results with the ground truth image showed that the coded aperture imaging technique has a cancerous pixel detection sensitivity (correct identification of cancerous pixels), specificity (correctly ruling out healthy pixels as not being cancer) and accuracy of 92.4%, 91.9% and 92.0%, respectively. Our Monte Carlo evaluation of our experimental coded aperture coherent scatter imaging system shows that it is able to exploit the greater contrast available from coherently scattered x-rays to increase the accuracy of detecting cancerous regions within the breast.

Paper Details

Date Published: 22 March 2016
PDF: 6 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 978321 (22 March 2016);
Show Author Affiliations
Manu N. Lakshmanan, Duke Univ. Medical Ctr. (United States)
Robert E. Morris, Duke Univ. Medical Ctr. (United States)
Joel A. Greenberg, Duke Univ. (United States)
Ehsan Samei, Duke Univ. Medical Ctr. (United States)
Duke Univ. (United States)
Anuj J. Kapadia, Duke Univ. Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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