Program Information
A Novel Molecular Breast Tomosynthesis System: Optimization of Acquisition Parameters
Matthew Trimble*, D Gilland, Univ Florida, Gainesville, FL
Presentations
SU-E-708-2 (Sunday, July 30, 2017) 1:00 PM - 1:55 PM Room: 708
Purpose: Nuclear medicine breast imaging using 99mTc-sestamibi and a dedicated gamma camera has demonstrated the potential to complement mammography in women with radio-dense breasts. We have developed a prototype gamma camera for breast imaging that uses limited angle tomography, an imaging method referred to as molecular breast tomosynthesis (MBT). The camera is equipped with a unique variable angle, slant-hole collimator that allows the camera to remain close to the breast throughout the tomographic acquisition. The purpose of this study is to use Monte Carlo simulation methods to efficiently determine optimal acquisition parameters for this prototype MBT system.
Methods: The Geant4 Application for Emission Tomography (GATE) Monte-Carlo simulation package was installed on the UF high performance computing facility and used to generate MBT projection images of a breast phantom over a 50 deg. angular range. The phantom contained spherical lesions of increased radioactivity uptake within a uniform background. The lesion and background images were generated independently and at a high count level to allow variable lesion-to-background activity levels and, via a count thinning process, variable noise level and angular dwell time. Projection images with variable angular sampling were also generated. These test data allow contrast-to-noise ratio measurements on the reconstructed MBT images.
Results: The simulated MBT projection images of the breast phantom qualitatively resembled experimental phantom images obtained with this prototype system. Reconstructed images using the iterative MLEM algorithm evidenced the variable lesion-to-background ratio, noise level, angular dwell time and angular sampling.
Conclusion: We have developed an efficient means of modeling a prototype MBT system using high performance computing and Monte Carlo simulation. These methods enable optimization of critical acquisition parameters for this system using contrast-to-noise measurements.
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