Absolute Quantification of Tc-99m Activity Distributions Using a Planar Molecular Breast Imaging Commercial System
Author ORCID Identifier
Date of Graduation
Doctor of Philosophy (PhD)
S. Cheenu Kappadath, Ph.D.
David T. A. Fuentes, Ph.D.
James P. Long, Ph.D.
Tinsu Pan, Ph.D.
Gaiane M. Rauch, M.D., Ph.D.
Molecular breast imaging (MBI) uses two dedicated-breast semiconductor detectors to visualize the preferential uptake of technetium-99m-sestamibi (99mTc-sestamibi) by breast cancer cells relative to surrounding benign breast tissues. Clinically, MBI is used primarily as a supplementary tool to standard-of-care mammography because of its improved detection of breast cancers, especially in women with mammographically-dense breasts. Because of a lack of image corrections, MBI applications are currently limited to qualitative evaluations of relative pixel intensities between image regions with suspected lesions and normal tissue.
The objective of this dissertation was to use Monte Carlo simulations to better characterize the MBI imaging process in order to develop data analysis techniques to accurately and absolutely quantify information on tumor 99mTc uptake using clinical MBI images. Using a wide range of simulated tumors in breast tissue with varying 99mTc uptake clinical levels, techniques were developed that are capable of quantifying tumor uptake diameters with an accuracy of 0.2 ± 1.9 mm (mean ± standard deviation) and tumor uptake total activities with an accuracy of 0.5% ± 11.1% (mean ± standard deviation). Throughout the development and testing of these techniques, particular care was taken to understand and mitigate possible sources of error to better estimate the performance of the techniques in future clinical applications. The dissertation concludes by demonstrating the feasibility, benefits, and challenges of implementing the proposed techniques in patient data as well as future applications of quantitative MBI measurements.
Molecular Breast Imaging, Quantitative Functional Imaging, Technetium-99m, Monte Carlo Simulations, Breast Cancer, Nuclear Medicine
Available for download on Thursday, June 01, 2023
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