With the fast development of photon counting detection techniques, spectral CT with a photon counting detector has attracted considerable attention by increasing energy-resolution to identify and discriminate materials. The conventional analytic reconstruction algorithms can be directly applied to reconstruct spectral CT images for each spectrum or energy bin. However, a comprehensive evaluation of analytic reconstruction algorithms for spectral CT has not been reported yet. This motivates us to evaluate the analytic spiral cone-beam CT algorithms and to provide a fair comparison platform for the state-of-the-art iterative spectral CT reconstruction algorithms. Considering the fact that a narrow energy bin has high noise which degrades the imaging quality of spectral CT, an adaptive maximum a posterior (MAP) projection restoration algorithm is first used to reduce the noise, and then a 2D/3D weighted spiral Feldkamp-Davis-Kress (FDK) algorithm is implemented to reconstruct the spectral CT images at different energy bins. Finally, the principle component analysis (PCA) is employed to render the spectral reconstruction results into a color space. Our numerical results show that the analytic reconstruction approach is fast and it can provide high spatial resolution, high contrast resolution and high signal-noise-ratio (SNR) under higher helical pitches. This makes it possible to serve as a platform to evaluate the state-of-the-art iterative spectral CT algorithms.
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