Accelerated Statistical Image Reconstruction Algorithms and Simplified Cost Functions for X-ray Computed Tomography.

In X-ray computed tomography (CT), bony structures cause beam-hardening artifacts that appear on the reconstructed image as streaks and shadows. Currently, there are two classes of methods for correcting for bone-related beam hardening. The standard approach used with filtered backprojection (FBP) reconstruction is the Joseph and Spital (JS) method [1]. In the current simulation study (which is inspired by a clinical head scan), the JS method requires a simple table or polynomial model for correcting water-related beam hardening, and two additional tuning parameters to compensate for bone. Like all FBP methods, it is sensitive to data noise. Statistical methods have also been proposed recently for image reconstruction from noisy polyenergetic X-ray data, [2], [3]. However, these methods have required more knowledge of the X-ray spectrum than is needed in the JS method, hampering their use in practice. This paper proposes a simplified statistical image reconstruction approach for polyenergetic X-ray CT that uses the same calibration data and tuning parameters used in the JS method, thereby facilitating its practical use. Simulation results indicate that the proposed method provides improved image quality (reduced beam hardening artifacts and noise) compared to the JS method, at the price of increased computation. The results also indicate that the image quality of the proposed method is comparable to a method requiring more beam-hardening information [3].