Empirical projection-based basis-component decomposition method

Bernhard Brendel; Ewald Roessl; Jens-Peter Schlomka; Roland Proksa

doi:10.1117/12.811006

13 March 2009 Empirical projection-based basis-component decomposition method

Bernhard Brendel, Ewald Roessl, Jens-Peter Schlomka, Roland Proksa

Proceedings Volume 7258, Medical Imaging 2009: Physics of Medical Imaging; 72583Y (2009) https://doi.org/10.1117/12.811006
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Advances in the development of semiconductor based, photon-counting x-ray detectors stimulate research in the domain of energy-resolving pre-clinical and clinical computed tomography (CT). For counting detectors acquiring x-ray attenuation in at least three different energy windows, an extended basis component decomposition can be performed in which in addition to the conventional approach of Alvarez and Macovski a third basis component is introduced, e.g., a gadolinium based CT contrast material. After the decomposition of the measured projection data into the basis component projections, conventional filtered-backprojection reconstruction is performed to obtain the basis-component images. In recent work, this basis component decomposition was obtained by maximizing the likelihood-function of the measurements. This procedure is time consuming and often unstable for excessively noisy data or low intrinsic energy resolution of the detector. Therefore, alternative procedures are of interest. Here, we introduce a generalization of the idea of empirical dual-energy processing published by Stenner et al. to multi-energy, photon-counting CT raw data. Instead of working in the image-domain, we use prior spectral knowledge about the acquisition system (tube spectra, bin sensitivities) to parameterize the line-integrals of the basis component decomposition directly in the projection domain. We compare this empirical approach with the maximum-likelihood (ML) approach considering image noise and image bias (artifacts) and see that only moderate noise increase is to be expected for small bias in the empirical approach. Given the drastic reduction of pre-processing time, the empirical approach is considered a viable alternative to the ML approach.

Citation Download Citation

Bernhard Brendel, Ewald Roessl, Jens-Peter Schlomka, and Roland Proksa "Empirical projection-based basis-component decomposition method", Proc. SPIE 7258, Medical Imaging 2009: Physics of Medical Imaging, 72583Y (13 March 2009); https://doi.org/10.1117/12.811006

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