  In computed tomography (CT), metal implants increase the inconsistencies
between the measured data and the linear attenuation assumption made by
analytic CT reconstruction algorithms. The inconsistencies give rise to dark
and bright bands and streaks in the reconstructed image, collectively called
metal artifacts. These artifacts make it difficult for radiologists to render
correct diagnostic decisions. We describe a data-driven metal artifact
reduction (MAR) algorithm for image-guided spine surgery that applies to
scenarios in which a prior CT scan of the patient is available. We tested the
proposed method with two clinical datasets that were both obtained during spine
surgery. Using the proposed method, we were not only able to remove the dark
and bright streaks caused by the implanted screws but we also recovered the
anatomical structures hidden by these artifacts. This results in an improved
capability of surgeons to confirm the correctness of the implanted pedicle
screw placements.
