Here we present a method for the removal of multi-material artifacts which occur during the application of a single material phase retrieval procedure to X-ray tomographic data sets. For the phase retrieval we chose the most common method which is the single material filter. The correction method which we describe in the following has been designed for samples consisting of three distinct materials, hence effectively two different material interfaces. Furthermore the material phase with the strongest X-ray interaction needs to show sufficient absorption in order to allow for segmenting this phase through application of a grey value threshold. If these conditions are fulfilled the method is easy to apply through post processing as is shown for the volume images of two sample types.

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How to evaluate and compare image quality from different sub-micrometer (subµ) CT scans? A simple yet clever test phantom is used for recording 13 scans in a number of commercial and some non-commercial scanners. From the resulting volume images, signal and noise power spectra are modeled for estimating spatial signalto-noise ratio (SNR spectrum). Using the same data, a time-and object-independent transfer function (MTF) is computed for each scan, including phase contrast strength and spatial resolution (MTF blur ). SNR and MTF are compared to transmission measurements of the same test phantom. Making SNR object-independent by normalization with respect to the object spectrum yields detection effectiveness (DE) a new measure which reveals how technical differences as well as operator-choices strongly influence scan quality for a given measurement time. Using DE both source-based and detector-based subµ CT scanners can be studied and optimized with respect to signal detection effectiveness using the metrics which are presented in this study. Future application of

In polychromatic x-ray imaging for nondestructive testing, material science or medical applications, image quality is usually a problem of detecting sample structure in noisy data. This problem is typically stated this way: As many photons as possible need to be detected to get a good image quality.We instead propose to use the concept of signal detection, which is more universal. In signal detection, it is the sample properties which are detected. Photons play the role of information carriers for the signal. Signal detection for example allows modeling the effects which polychromaticity has on image quality.SNR spectra (= spatial SNR) are used as a quantity to describe if reliable signal detection is possible. They include modulation transfer and phase contrast in addition to noisiness effects. SNR spectra can also be directly measured, which means that theoretical predictions can easily be tested.We investigate the effects of signal and noise superposition on the SNR spectrum and show how selectively not detecting photons can increase the image quality.

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