Evaluation of an Analytic Reconstruction Method as a Platform for Spectral Cone-Beam CT

With the fast development of photon counting detection techniques, spectral computed tomography (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 projection restoration algorithm is first used to reduce the noise, and then, a 2-D/3-D weighted spiral Feldkamp-Davis-Kress algorithm is implemented to reconstruct the spectral CT images at different energy bins. Finally, the principle component analysis 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-to-noise-ratio under higher helical pitches. This makes it possible to serve as a platform to evaluate the state-of-the-art iterative spectral CT algorithms.