Investigation of maximum a posteriori probability expectation-maximization for image-based weighting spectral X-ray CT image reconstruction.

Development of spectral X-ray computer tomography (CT) equipped with photon counting detector has been recently attracting great research interest. This work aims to improve the quality of spectral X-ray CT image. Maximum a posteriori (MAP) expectation-maximization (EM) algorithm is applied for reconstructing image-based weighting spectral X-ray CT images. A spectral X-ray CT system based on the cadmium zinc telluride photon counting detector and a fat cylinder phantom were simulated. Comparing with the commonly used filtered back projection (FBP) method, the proposed method reduced noise in the final weighting images at 2, 4, 6 and 9 energy bins up to 85.2%, 87.5%, 86.7% and 85%, respectively. CNR improvement ranged from 6.53 to 7.77. Compared with the prior image constrained compressed sensing (PICCS) method, the proposed method could reduce noise in the final weighting images by 36.5%, 44.6%, 27.3% and 18% at 2, 4, 6 and 9 energy bins, respectively, and improve the contrast-to-noise ratio (CNR) by 1.17 to 1.81. The simulation study also showed that comparing with the FBP and PICCS algorithms, image-based weighting imaging using MAP-EM statistical algorithm yielded significant improvement of the CNR and reduced the noise of the final weighting image.