Evaluation of an image-based algorithm for quantitative spectral CT applications

In this paper we describe and evaluate an image-based spectral CT method. Its central formula expresses measured CT data as a spectral integration of the spectral attenuation coefficient multiplied by a Local Weighting Function (LWF). The LWF represents the local energy weighting in the image domain, taking into account the system and reconstruction properties and the object self attenuation. A generalized image-based formulation of spectral CT algorithms is obtained, with no need for additional corrections of e.g. beam hardening. The iterative procedure called Local Spectral Reconstruction (LSR) yields both the mass attenuation coefficients of the object and a representation of the LWF. The quantitative accuracy and precision of the method is investigated in several applications, including beam hardening correction, attenuation correction for SPECT/CT and PET/CT and a direct identification of spectral attenuation functions using the LWF result is demonstrated. In all applications the ground truth of the objects is reproduced with a quantitative accuracy in the sub-percent to two percent range. An exponential convergence behavior of the iterative procedure is observed, with one to two iteration steps as a good compromise between quantitative accuracy and precision. We conclude that the method can be used to perform image-based spectral CT reconstructions with quantitative accuracy. Existing algorithms benefit from the intrinsic treatment of beam hardening and system properties. Novel algorithms are enabled to directly compare material model functions to spectral measurement data.