Seemeen Karimi1, Harry Martz2, Pamela Cosman1. 1. University of California, San Diego, La Jolla, San Diego, CA, USA. 2. Lawrence Livermore National Laboratories, Livermore, California, San Francisco, CA, USA.
Abstract
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.
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.
Entities:
Keywords:
Metal artifacts; computed tomography; constrained optimization; luggage screening; metal artifact reduction