Development of a Monte Carlo Simulation Method for use in investigating CT (Computed Tomography) Mammography

Thacker, Samta C

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Development of a Monte Carlo Simulation Method for use in investigating CT (Computed Tomography) Mammography

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The development of new digital mammography techniques such as dual-energy imaging, tomosynthesis and CT mammography will require investigation of optimal camera design parameters and optimal imaging acquisition parameters. One tool that is useful for this purpose is Monte Carlo simulation. This study presents a methodology for generating simulated images from a CsI-based, flat-panel imager model and for estimating the normalized glandular dose to the uncompressed breast in CT mammography. The simulation uses the GEANT 3 Monte Carlo code to model x-ray transport and absorption within the CsI scintillator, and the DETECT-II code to track optical photon spread within a columnar model of the CsI scintillator. The Monte Carlo modeling of x-ray transport and absorption within the CsI was validated by comparing to previously published values for the probability of a K-shell interaction, the fluorescent yield, the probability of a K-fluorescent emission, and the escape fraction describing the probability of a K x-ray escaping the scintillator. To validate the combined (GEANT 3 coupled with DETECT-II) Monte Carlo approach to form simulated images, comparison of modulation transfer functions (MTFs) and system sensitivity (electrons/mR/pixel) obtained from simulations were compared to empirical measurements obtained with different x-ray spectra and imagers with varying CsI thicknesses. By varying the absorption and reflective properties of the columnar CsI used in the DETECT-II code, good agreement between simulated MTFs and system sensitivity and empirically measured values were observed. The Monte Carlo software was also validated for dosimetry by comparing results of the linear attenuation coefficient values and the normalized glandular dose (DgN) values of the compressed breast, to those reposted in the literature. The normalized glandular dose was then estimated for three different sizes of the uncompressed breast with a homogeneous composition of adipose and glandular tissue. Further, fit equations of the normalized glandular dose curves were also generated using MATLAB. These equations can be used to replicate the dose for the three sizes of the breast and three compositions of the adipose and glandular tissue. In addition, images displaying energy deposition maps are presented to better understand the spatial distribution of dose in CT mammography.

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