  CT images have been used to generate radiation therapy treatment plans for
more than two decades. Dual-energy CT (DECT) has shown high accuracy in
estimating electronic density or proton stopping-power maps used in treatment
planning. However, the presence of metal implants introduces severe streaking
artifacts in the reconstructed images, affecting the diagnostic accuracy and
treatment performance. In order to reduce the metal artifacts in DECT, we
introduce a metal-artifact reduction scheme for iterative DECT algorithms. An
estimate is substituted for the corrupt data in each iteration. We utilize
normalized metal-artifact reduction (NMAR) composed with image-domain
decomposition to initialize the algorithm and speed up the convergence. A fully
3D joint statistical DECT algorithm, dual-energy alternating minimization
(DEAM), with the proposed scheme is tested on experimental and clinical helical
data acquired on a Philips Brilliance Big Bore scanner. We compared DEAM with
the proposed method to the original DEAM and vendor reconstructions with and
without metal-artifact reduction for orthopedic implants (O-MAR). The
visualization and quantitative analysis show that DEAM with the proposed method
has the best performance in reducing streaking artifacts caused by metallic
objects.
