BACKGROUND: In aviation security, checked luggage is screened by
computed tomography scanning. Metal objects in the bags create artifacts
that degrade image quality. Though there exist metal artifact reduction
(MAR) methods mainly in medical imaging literature, they require knowledge
of the materials in the scan, or are outlier rejection methods.
OBJECTIVE: To improve and evaluate a MAR method we previously
introduced, that does not require knowledge of the materials in the scan,
and gives good results on data with large quantities and different kinds of
metal.
METHODS: We describe in detail an optimization which de-emphasizes
metal projections and has a constraint for beam hardening and scatter. This
method isolates and reduces artifacts in an intermediate image, which is then
fed to a previously published sinogram replacement method. We evaluate the
algorithm for luggage data containing multiple and large metal objects. We
define measures of artifact reduction, and compare this method against
others in MAR literature.
RESULTS: Metal artifacts were reduced in our test images, even for
multiple and large metal objects, without much loss of structure or
resolution.
CONCLUSION: Our MAR method outperforms the methods with which we
compared it. Our approach does not make assumptions about image content, nor
does it discard metal projections.
