Share Email Print

Proceedings Paper

Investigation of optimal parameters for penalized maximum-likelihood reconstruction applied to iodinated contrast-enhanced breast CT
Author(s): Andrey Makeev; Lynda Ikejimba; Joseph Y. Lo; Stephen J. Glick
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Although digital mammography has reduced breast cancer mortality by approximately 30%, sensitivity and specificity are still far from perfect. In particular, the performance of mammography is especially limited for women with dense breast tissue. Two out of every three biopsies performed in the U.S. are unnecessary, thereby resulting in increased patient anxiety, pain, and possible complications. One promising tomographic breast imaging method that has recently been approved by the FDA is dedicated breast computed tomography (BCT). However, visualizing lesions with BCT can still be challenging for women with dense breast tissue due to the minimal contrast for lesions surrounded by fibroglandular tissue. In recent years there has been renewed interest in improving lesion conspicuity in x-ray breast imaging by administration of an iodinated contrast agent. Due to the fully 3-D imaging nature of BCT, as well as sub-optimal contrast enhancement while the breast is under compression with mammography and breast tomosynthesis, dedicated BCT of the uncompressed breast is likely to offer the best solution for injected contrast-enhanced x-ray breast imaging. It is well known that use of statistically-based iterative reconstruction in CT results in improved image quality at lower radiation dose. Here we investigate possible improvements in image reconstruction for BCT, by optimizing free regularization parameter in method of maximum likelihood and comparing its performance with clinical cone-beam filtered backprojection (FBP) algorithm.

Paper Details

Date Published: 25 March 2016
PDF: 7 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 978327 (25 March 2016); doi: 10.1117/12.2217438
Show Author Affiliations
Andrey Makeev, U.S. Food and Drug Administration (United States)
Lynda Ikejimba, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Joseph Y. Lo, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. (United States)
Stephen J. Glick, U.S. Food and Drug Administration (United States)

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

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?