Recent Approaches to Metal Artifact Reduction in X-Ray CT Imaging

Soomin Jeon () and Chang-Ock Lee ()
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Soomin Jeon: Massachusetts General Hospital and Harvard Medical School, Department of Radiology
Chang-Ock Lee: KAIST, Department of Mathematical Sciences

Chapter 10 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 347-377 from Springer

Abstract: Abstract Metal artifacts severely degrade image quality by generating streak artifacts in X-ray computed tomography (CT) images. Metal artifact reduction (MAR) has long been an important issue because metal artifacts interfere with the acquisition of accurate contrast images, limiting the various applications of CT imaging. In this work, three recently developed CT MAR methods are introduced: normalized MAR, surgery-based MAR, and convolutional neural network-based MAR. Also, a MAR method for industrial cone beam CT is presented as an industrial application.

Keywords: Computed tomography (CT); Convolutional neural network (CNN); Normalized metal artifact reduction (NMAR); Sinogram; Surgery based metal artifact reduction (SMAR) (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-030-98661-2_114

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