Visual grading evaluation of commercially available metal artefact reduction techniques in hip prosthesis computed tomography.
OBJECTIVE
To evaluate metal artefact reduction (MAR) techniques from four CT vendors in hip prosthesis imaging.


METHODS
Bilateral hip prosthesis phantom images, obtained by using MAR algorithms for single-energy CT data or dual-energy CT (DECT) data and by monoenergetic reconstructions of DECT data, were visually graded by five radiologists using 10 image quality criteria. Comparisons between the MAR images and a reference image were performed for each scanner separately. Ordinal probit regression analysis was used.


RESULTS
The MAR algorithms in general improved the image quality based on the majority of the criteria (up to between 8/10 and 10/10) with a statistical improvement in overall image quality (p < 0.001). However, degradation of image quality, such as new artefacts, was seen in some cases. A few monoenergetic reconstruction series improved the image quality (p < 0.004) for one of the DECT scanners, but it was only improved for some of the criteria (up to 5/10). Monoenergetic reconstructions resulted in worse image quality for the majority of the criteria (up to 7/10) for the other DECT scanner.


CONCLUSIONS
The MAR algorithms improved the image quality of the hip prosthesis CT images. However, since additional artefacts and degradation of image quality were seen in some cases, all algorithms should be carefully evaluated for every clinical situation. Monoenergetic reconstructions were in general concluded to be insufficient for reducing metal artefacts.


CONCLUSIONS
Qualitative evaluation of the usefulness of several MAR techniques from different vendors in CT imaging of hip prosthesis.
1 Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.|2 School of Health and Medical Sciences, Örebro University, Örebro, Sweden.
Karin M
KM
Andersson
1 Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Eva
E
Norrman
2 School of Health and Medical Sciences, Örebro University, Örebro, Sweden.|3 Department of Radiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Håkan
H
Geijer
2 School of Health and Medical Sciences, Örebro University, Örebro, Sweden.|3 Department of Radiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Wolfgang
W
Krauss
4 Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden.|5 Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Yang
Y
Cao
2 School of Health and Medical Sciences, Örebro University, Örebro, Sweden.|3 Department of Radiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Johan
J
Jendeberg
3 Department of Radiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.|6 Department of Clinical Sciences, Lund University, Lund, Sweden.
Mats
M
Geijer
3 Department of Radiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Mats
M
Lidén
1 Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.|2 School of Health and Medical Sciences, Örebro University, Örebro, Sweden.
Per
P
Thunberg
D000465:Algorithms; D016477:Artifacts; D006621:Hip Joint / Q000000981:diagnostic imaging*; D006622:Hip Prosthesis; D006801:Humans; D008670:Metals; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed
D008670:Metals