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Metal artifact reduction for CT: Development, implementation, and clinical comparison of a generic and a scanner‐specific technique
Author(s) -
Joemai Raoul M. S.,
de Bruin Paul W.,
Veldkamp Wouter J. H.,
Geleijns Jacob
Publication year - 2012
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 - 10.1118/1.3679863
Subject(s) - scanner , artifact (error) , image quality , computer science , reduction (mathematics) , computer vision , artificial intelligence , projection (relational algebra) , noise reduction , noise (video) , nuclear medicine , image (mathematics) , medicine , mathematics , algorithm , geometry
Purpose : To develop, implement, and compare two metal artifact reduction methods for CT. Methods : Two methods for metal artifact reduction were developed. The first is based on applying corrections in a Radon transformation of the CT images. The second method is based on a forward projection of the CT images and applying corrections in the scanner's original raw data. The first method is generic since it does not depend on the scanner specifications. For the second method, detailed information on the design of the CT scanner and the raw data of the study is required. Clinical implementation and evaluation were performed using pre‐ and post‐operative CT scans of four patients with shoulder prosthesis. For comparison of these methods, the authors developed a quantitative technique that compares improvement in image quality for the two metal artifact reduction techniques with the image quality of the uncorrected images. Results : Metal artifact reduction using either of the two methods yields a decrease of noise and artifacts in CT scans of patients with shoulder prostheses. Artifacts that appeared as bright and dark streaks were reduced or eliminated and as a result image quality improved. Quantitative assessment of clinical images showed improved image quality for both techniques of metal artifact reduction, but the method based on correction in original raw data performed better in all comparisons. Conclusion : Both methods are effective for metal artifact reduction, but better performance was observed for the method that is based on correcting the original raw data. The used evaluation technique provides an objective way of evaluating the metal artifacts in clinical CT images.