Spectral CT reconstruction with an explicit photon-counting detector model: a one-step approach

Pierre-Antoine Rodesch; V. Rebuffel; C. Fournier; F. Forbes; L. Verger

doi:10.1117/12.2285792

9 March 2018 Spectral CT reconstruction with an explicit photon-counting detector model: a one-step approach

Pierre-Antoine Rodesch, V. Rebuffel, C. Fournier, F. Forbes, L. Verger

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 1057353 (2018) https://doi.org/10.1117/12.2285792
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Recent developments in energy-discriminating Photon-Counting Detector (PCD) enable new horizons for spectral CT. With PCDs, new reconstruction methods take advantage of the spectral information measured through energy measurement bins. However PCDs have serious spectral distortion issues due to charge-sharing, fluorescence escape, pileup effect… Spectral CT with PCDs can be decomposed into two problems: a noisy geometric inversion problem (as in standard CT) and an additional PCD spectral degradation problem. The aim of the present study is to introduce a reconstruction method which solves both problems simultaneously: a “one-step” approach. An explicit linear detector model is used and characterized by a Detector Response Matrix. The algorithm reconstructs two basis material maps from energy-window transmission data. The results prove that the simultaneous inversion of both problems is well performed for simulation data. For comparison, we also perform a standard “two-step” approach: an advanced polynomial decomposition of measured sinograms combined with a filtered-back projection reconstruction. The results demonstrate the potential uses of this method for medical imaging or for non-destructive testing in industry.

Citation Download Citation

Pierre-Antoine Rodesch, V. Rebuffel, C. Fournier, F. Forbes, and L. Verger "Spectral CT reconstruction with an explicit photon-counting detector model: a one-step approach", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057353 (9 March 2018); https://doi.org/10.1117/12.2285792

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