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Metal artifact reduction in CT using tissue‐class modeling and adaptive prefiltering
Author(s) -
Bal Matthieu,
Spies Lothar
Publication year - 2006
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2218062
Subject(s) - streak , imaging phantom , artificial intelligence , computer vision , computer science , artifact (error) , medical imaging , image registration , iterative reconstruction , reduction (mathematics) , noise (video) , image (mathematics) , pattern recognition (psychology) , nuclear medicine , medicine , mathematics , optics , physics , geometry
High‐density objects such as metal prostheses, surgical clips, or dental fillings generate streak‐like artifacts in computed tomography images. We present a novel method for metal artifact reduction by in‐painting missing information into the corrupted sinogram. The information is provided by a tissue‐class model extracted from the distorted image. To this end the image is first adaptively filtered to reduce the noise content and to smooth out streak artifacts. Consecutively, the image is segmented into different material classes using a clustering algorithm. The corrupted and missing information in the original sinogram is completed using the forward projected information from the tissue‐class model. The performance of the correction method is assessed on phantom images. Clinical images featuring a broad spectrum of metal artifacts are studied. Phantom and clinical studies show that metal artifacts, such as streaks, are significantly reduced and shadows in the image are eliminated. Furthermore, the novel approach improves detectability of organ contours. This can be of great relevance, for instance, in radiation therapy planning, where images affected by metal artifacts may lead to suboptimal treatment plans.