Metal artifacts are increasingly prevalent in CT reconstructed volumes due to the presence of implants in the aging population. High degree of beam hardening in severely attenuating objects is one of the main contributors of strong metal artifacts. In this paper, we propose a method to reduce metal artifacts due to beam hardening by enforcing consistency conditions on the uncorrected polychromatic projections. Our results from clinical datasets show the reduction of artifacts after the proposed correction.
The polychromatic X-ray spectrum and the energy-dependent material attenuation coefficients generate beam hardening artifacts in CT reconstructed images. The artifacts can be corrected by projection linearization using polynomials. Recently, consistency conditions derived from Grangeat’s fundamental relation have been successfully employed for estimating the correction polynomials without calibration or prior knowledge. In this paper, we show that the polynomials can also be computed by enforcing pair-wise fan beam consistency conditions on cone beam projections. Our preliminary results from simulation and real data experiments show the significant reduction of first-order artifacts after correction with the proposed method.
Due to wide cone angle, the artifacts caused by scatter radiation are inevitable in flat detector CT reconstructed images. Cupping and streak artifacts are the main manifestations of scatter artifacts which will degrade low contrast resolution and Hounsfield unit accuracy. Scatter artifacts can be mitigated by subtracting the two-dimensional distribution of scatter radiation from the measured projections. Convolution-based scatter modeling can be used for the approximate scatter estimation with a high degree of computational efficiency. In this paper, we propose an algorithm to optimize the scatter kernel parameters by enforcing pair-wise fan beam consistency conditions on cone beam projections. The proposed method does not require prior Monte-Carlo simulation, additional reconstruction, or calibration experiments. Our results from the simulated datasets show the reduction of artifacts after the minimization of scatter-induced data inconsistency.