Metal artifact reduction for CT-based luggage screening

2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) METAL ARTIFACT REDUCTION FOR CT-BASED LUGGAGE SCREENING Seemeen Karimi, Pamela Cosman Harry Martz University of California, San Diego Dept. of Electrical and Computer Engineering 9500 Gilman Dr., La Jolla, CA 92093-0407 Lawrence Livermore National Laboratories Center for Nondestructive Testing 7000 East Ave., Livermore, CA 94550 ABSTRACT In aviation security, checked luggage is screened by com- puted tomography (CT) scanning, followed by automatic tar- get recognition from the CT images. Metal objects in the bags cause image artifacts that degrade object representation, leading to increased false alarms. We develop a new method, which isolates and reduces artifacts in an intermediate image, based on a numerical optimization that de-emphasizes metal and has a novel constraint for beam hardening and scatter. Re- sults on test bags showed excellent artifact reduction, even for multiple metal objects. Index Terms— metal artifact reduction, computed to- mography, luggage screening, constrained optimization 1. INTRODUCTION In aviation security, checked luggage is scanned by explo- sives detection systems (EDS). Many EDS are based on x-ray computed tomography (CT). Automatic target recognition al- gorithms in these systems analyze the CT images for threats. Metal objects present in the luggage create image artifacts ap- pearing as shadows or streaks. These artifacts misrepresent the surrounding objects, and may lead to apparent splitting of single objects, or the merging of separate objects. Reducing the metal artifacts will likely lead to lower false alarms [1]. Metal artifacts are caused by beam hardening, photon scatter, partial volume effects, photon starvation, and data sampling errors [2, 3]. Beam hardening and scatter cause low-frequency artifacts [2], which are more difﬁcult to re- move, while the other sources result in narrow streaks. Algorithms for metal artifact reduction (MAR) have been developed in medical CT imaging since the 1980s [4]. De- spite the advances, there are no widely accepted solutions, and MAR continues to be a challenging research problem. There are three main approaches - sinogram replacement [4– 15], energy decomposition with multiple scanning spectra, e.g., [16], and iterative reconstruction (IR) [17–20]. All these methods operate in Radon space (also called projections or sinograms). Sinogram replacement has been the most explored be- cause of its low complexity. In methods based on sinogram 978-1-4799-2893-4/14/$31.00 ©2014 IEEE replacement, a ﬁltered-backprojection (FBP) image is recon- structed from scanner projections, and the metal objects are identiﬁed by image segmentation techniques [8,21]. The pro- jection data corresponding to rays that pass through metal (called traces) are identiﬁed by calculation, by forward pro- jection (reprojection) of the metals or even by segmentation in the sinogram. Metal trace data in the sinogram are replaced with an estimate of underlying data, and the corrected sino- gram is reconstructed by FBP. It is difﬁcult to estimate the un- derlying data accurately. Interpolation across the metal traces removes edges from high-contrast structures and renders the projections inconsistent, leading to secondary artifacts. In re- cent years in medical MAR, image segmentation has been used to identify high-contrast structures, to develop an inter- mediate image that is called a prior-image [8, 12, 14, 22]. The prior-image is reprojected and thus used to guide data replace- ment in the scanner sinogram. In luggage, image segmenta- tion cannot separate artifacts from data, because assumptions cannot be made regarding the contents of the images, and be- cause more metal and more artifact interference are present in luggage images. IR algorithms, such as those based on expectation maxi- mization [23] or algebraic reconstruction technique [24], are used in x-ray CT to reconstruct data with poor signal-to-noise ratio, and where there is missing data [25]. A recent approach is to use IR or numerical optimization for MAR [13, 26, 27] in medical imaging. These algorithms discard all metal trace data. IR and numerical reconstructions usually result in a loss of texture or resolution, so in [13], the optimum solution is used as a prior-image, which is then reprojected, and metal traces from the original sinogram are replaced with the repro- jected traces. In luggage screening, a third or even half the sinogram may contain metal. If all these data are discarded, the recon- structions are poor, as we will demonstrate. Our approach is also hybrid in that it reconstructs a prior-image by optimiza- tion, followed by sinogram replacement and FBP. However, we retain the metal projection data with reduced weights, and add a new constraint. Our target prior-image is artifact-free and sparse. The ﬁnal image has the texture and resolution of FBP reconstruction.