An adaptive decomposition algorithm for quantitative spectral CT imaging

With the development of photon counting detectors (PCDs), spectral CT achieves higher performance than in the past, thus has better application prospects. In this paper, an adaptive dual effect decomposition (ADED) algorithm is proposed to perform the quantitative estimation of effective atomic number and electron density. The algorithm is an empirical material identification algorithm that achieves calibration by combining polynomials on the projection data and constraining in the image domain. In addition, we also propose an innovative effective atomic number estimation model with an accurate physical model that adaptively adjusts the parameters according to the x-ray energy and material type. The performance of the algorithm was verified by experiment and simulation in this paper. Compared to the conventional basis material decomposition algorithm, the proposed ADED algorithm reduces the relative error in the quantification of the effective atomic number from 4.5%-12.0% to 0.3%-4.5%. Overall, the results demonstrate that the proposed algorithm significantly improves the accuracy of quantifying the effective atomic number.