Acceleration of the Scattering Noise Model Based Image Reconstruction Algorithm for XFCT

Current benchtop X-ray fluorescence computed tomography (XFCT) system, which uses polychromatic x-ray source produced by conventional x-ray tubes, suffers from statistical noise caused by Compton scatter background. As the statistical noise caused by scattered photons is difficult to be removed and will contaminate the XRF signal, a noise model based reconstruction algorithm might be necessary for XFCT image reconstruction. In our previous study, we presented an EM iteration algorithm for benchtop XFCT image reconstruction based on Poisson noise model. The estimation of the scatter background and the XRF signal was updated alternately during each iteration. The iteration algorithm works well in reducing the scattering noise and improving the contrast of the target element compared with traditional ML-EM algorithm but requires more iteration steps. In this study, we presented an acceleration strategy of the scattering noise model based EM iteration. Numerical simulations indicate that the convergence was accelerated and the number of iterations required was significantly reduced.