Room: AAPM ePoster Library
Purpose: The main goal of this study was to develop a theoretical framework, which allows for the investigation of x-ray spectra shape effects on the resulting dual-energy (DE) image quality, quantified in terms of the contrast-to-noise ratio (CNR) of DE image.
Methods: Spectrum shape is described with an analytical function, which depicts the physical mechanisms of the Bremsstrahlung radiation and its attenuation inside the anode. Such function is described by four parameters: tube potential E???, total photon fluence a, and two shape parameters. It can be re-written in terms of average spectrum energy E??? as well.
The equation for CNR is derived as a function of a spectrum parameters of each individual energy. This equation consists of several parts, describing contrast and noise, allowing for their independent assessment.
The optimization of the CNR function was conducted in order to determine the optimal spectra parameters. A total dose to the patient was used as a limiting factor. The CNR function was analyzed and general requirements for its maximization were defined.
Results: It was shown that the contrast part of the CNR equation increases with the increase of the ratio between spectra average energies (energy separation). However, the noise part of the equation has a more complicated dependency on spectra parameters, which results in constraints on spectrum width and energy separation. An additional constrain comes from dose limitations.
Conclusion: The theoretical description of the CNR of the DE image was developed. The derived CNR function allows for optimization of the spectra parameters and their impact on the image contrast and noise were evaluated. It was shown that the commonly used approach of maximizing the energy separation ratio can increase the contrast but introduce higher noise. This approach can be used for an optimization of the spectrum shape independent on the x-ray source type.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the ACOA (Atlantic Canada Opportunities Agency), Atlantic Innovation Fund #208515.