A new statistical image reconstruction algorithm for polyenergetic X-ray CT

Abstract

This paper presents a new statistical reconstruction algorithm for X-ray CT. The algorithm is based on Poisson statistics and a physical model that accounts for the measurement nonlinearities caused by energy-dependent attenuation. We model each voxel's attenuation as a mixture of bone and soft tissue by defining density-dependent tissue fractions, maintaining one unknown per voxel avoiding the need of a pre-segmentation. Rather than requiring the entire X-ray spectrum, the method approximates the 2D beam hardening function corresponding to bone and soft tissue with the 1D function corresponding to water and one or two empirical tuning parameters.Results on simulated human data (NCAT phantom) showed a beam hardening reduction similar to conventional post-processing techniques, but with an improved signal to noise ratio.

Complete scheme for beam hardening correction in small animal computed tomography

“…To this end, we need to find the amount of water that would have attenuation properties equivalent to each combination of water and bone traversed, so that FBHZD(tw, tb) = FBHZD(tw + (J(tw, tb), 0) = FBH1D(te) (2) where the line integral of the equivalent water path is given by te = tw + (J(tw, tb)' Following the ideas in [7,12,13], we approximate (J(tw, tb) to a second order polynomial only dependent on the amount of bone traversed by the X-rays:…”

“…Other strategies like dual energy [5,6], post-processing [7][8][9] and reconstruction with iterative methods [10][11][12] have been proposed. The main drawbacks of these approaches are that the dual energy technique requires a sophisticated hardware and more radiation dose and the iterative methods involve high computational burden.…”

Simplified Statistical Image Reconstruction for X-ray CT With Beam-Hardening Artifact Compensation

“…In an earlier conference paper [20], we explored in more depth the work in [19], proposed an improved approximation function, and presented preliminary 2D simulation results. The method modeled the tissue fractions in the voxels as functions of the density similarly to what was done in [18].…”

Metal Artifact Reduction in X-ray CT via Ray Profile Correction

“…These efforts can be largely divided into two types of approaches: iterative reconstruction and sinogram correction. The iterative reconstruction methods adapt existing X-ray CT systems by incorporating one or more of the following types of a-priori knowledge: (1) low-level information of the images to be reconstructed [5,6,7,8], (2) the X-ray spectrum of the source [9], (3) the attenuation functions of the base materials [10,11,12], and (4) the composition of the metal components [13]. More recently proposed iterative algorithms attempt to reduce beam hardening effects without the need of any prior knowledge by decomposing the image to be reconstructed into low and high density components [14,15].…”