Room: AAPM ePoster Library
Purpose: aim of this study is to develop an algorithm to find optimal filter (material and thickness) and x-ray tube (current–mAs and potential–kVp) parameters for low- and high-energy (LE, HE) beams for dual-energy (DE) imaging systems, allowing for achieving maximum contrast-to-noise ratio (CNR). The algorithm takes into account imaging system constrains (mAs, kVp, detector dynamic range), clinical limitations (dose, patient size), and scatter effects.
Methods: algorithm utilizes derived analytical expression for DE CNR which allows for evaluation of various beam/filter parameters. X-ray spectra for each combination of kVp, mAs, filter material, and filter thickness were simulated with a customized Spektr 3.0 software. Scatter was assessed with a previously developed Monte Carlo model by obtaining scatter-to-primary ratios for different regions of interests and included in CNR calculations.
Dynamic range of flat panel detector, patient sizes, and dose limitations were included using data from previous studies. For each combination (in total over 10?) of parameters, patient surface dose was calculated and LE and HE signals were evaluated for compliance with the detector dynamic range. All analysis was performed for bone and soft tissue DE images and three patient sizes: small, medium, and large.
Results: optimal filtration pair was identified for each patient size and reduced to a combination of two materials with different thicknesses, namely: 4747Ag, 1.0mm for HE and ²²22Ti, 0.4mm for LE. Optimal beam parameters, kVp(mAs) were:
Soft tissue images: small 140(85)–60(90), medium 140(90)–70(90), and large 140(90)–90(75).
Bone images: small 140(85)–60(90), medium 140(90)–70(90), and large 140(90)–80(90).
Conclusion: algorithm, which provides an optimal combination of beam and filter parameters, leading to the maximized CNR of DE images, was developed and tested. Optimization was performed with limitations of a clinically available imaging system, but the algorithm can be implemented for any system.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the ACOA (Atlantic Canada Opportunities Agency), Atlantic Innovation Fund #208515.