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In polychromatic x-ray imaging for nondestructive testing, material scienceor medical applications, image quality is usually a problem of detecting samplestructure in noisy data. This problem is typically stated this way: As manyphotons as possible need to be detected to get a good image quality.  We instead propose to use the concept of signal detection, which is moreuniversal. In signal detection, it is the sample properties which are detected.Photons play the role of information carriers for the signal. Signal detectionfor example allows modeling the effects which polychromaticity has on imagequality.  $\mathit{SNR}$ spectra (= spatial $\mathit{SNR}$) are used as a quantity todescribe if reliable signal detection is possible. They include modulationtransfer and phase contrast in addition to noisiness effects. $\mathit{SNR}$spectra can also be directly measured, which means that theoretical predictionscan easily be tested.  We investigate the effects of signal and noise superposition on the$\mathit{SNR}$ spectrum and show how selectively not detecting photons canincrease the image quality.

SNR Spectra as a Quantitative Model for Image Quality in Polychromatic X-Ray Imaging