| 1. |
- Ehn, Sebastian, et al.
(författare)
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X-ray deconvolution microscopy
- 2016
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Ingår i: Biomedical Optics Express. - 2156-7085. ; 7:4, s. 1227-1239
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Tidskriftsartikel (refereegranskat)abstract
- Recent advances in single-photon-counting detectors are enabling the development of novel approaches to reach micrometer-scale resolution in x-ray imaging. One example of such a technology are the MEDIPIX3RX-based detectors, such as the LAMBDA which can be operated with a small pixel size in combination with real-time on-chip charge-sharing correction. This characteristic results in a close to ideal, box-like point spread function which we made use of in this study. The proposed method is based on raster-scanning the sample with sub-pixel sized steps in front of the detector. Subsequently, a deconvolution algorithm is employed to compensate for blurring introduced by the overlap of pixels with a well defined point spread function during the raster-scanning. The presented approach utilizes standard laboratory x-ray equipment while we report resolutions close to 10 mu m. The achieved resolution is shown to follow the relationship p/n with the pixel-size p of the detector and the number of raster-scanning steps n. (C) 2016 Optical Society of America
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| 2. |
- Hahn, Dieter, et al.
(författare)
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Statistical iterative reconstruction algorithm for X-ray phase-contrast CT.
- 2015
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- Grating-based phase-contrast computed tomography (PCCT) is a promising imaging tool on the horizon for pre-clinical and clinical applications. Until now PCCT has been plagued by strong artifacts when dense materials like bones are present. In this paper, we present a new statistical iterative reconstruction algorithm which overcomes this limitation. It makes use of the fact that an X-ray interferometer provides a conventional absorption as well as a dark-field signal in addition to the phase-contrast signal. The method is based on a statistical iterative reconstruction algorithm utilizing maximum-a-posteriori principles and integrating the statistical properties of the raw data as well as information of dense objects gained from the absorption signal. Reconstruction of a pre-clinical mouse scan illustrates that artifacts caused by bones are significantly reduced and image quality is improved when employing our approach. Especially small structures, which are usually lost because of streaks, are recovered in our results. In comparison with the current state-of-the-art algorithms our approach provides significantly improved image quality with respect to quantitative and qualitative results. In summary, we expect that our new statistical iterative reconstruction method to increase the general usability of PCCT imaging for medical diagnosis apart from applications focused solely on soft tissue visualization.
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