Spectrum optimization for computed radiography systems

Johann Hummel; Friedrich Semturs; Marcus Kaar; Peter Homolka; Michael Figl

doi:10.1117/12.2044042

19 March 2014 Spectrum optimization for computed radiography systems

Johann Hummel, Friedrich Semturs, Marcus Kaar, Peter Homolka, Michael Figl

Proceedings Volume 9033, Medical Imaging 2014: Physics of Medical Imaging; 903349 (2014) https://doi.org/10.1117/12.2044042
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Technical quality assurance (TQA) is one of the key issues in breast screening protocols where the two crucial aspects are image quality and dose. While digital radiography (DR) systems can produce excellent image quality at low dose, it appears often to be difficult with computed radiography (CR) systems to fulfill the requirements for image quality and to keep the dose below the limits. Here, the choice of the optimal spectrum can be necessary to comply with the limiting values given by the standards. To determine the optimal spectrum, we calculated the contrast-noise ratio (CNR) for different anode/filter (a/f) combinations in dependence of tube voltage. This was done for breast thicknesses of 50, 60 and 70 mm. The figure-of-merit to be optimized was the quotient of squared CNR and average glandular dose. The investigated imaging plates were made of BaFBrI:Eu from a Fuji CR system. For comparison we repeated the measurements on a Carestream system. With respect to the Fuji system we found that the two k-edges of Iodine at 33 kV and Barium at 37 kV influence the results significantly. A peak as found in DR systems is followed by two additional peaks resulting from the higher absorption at the k-edges. This can be experienced with all a/f combinations. The same effect also occurred on the Carestream system.

Citation Download Citation

Johann Hummel, Friedrich Semturs, Marcus Kaar, Peter Homolka, and Michael Figl "Spectrum optimization for computed radiography systems", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 903349 (19 March 2014); https://doi.org/10.1117/12.2044042

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