PURPOSE: To assess the value of iterative frequency split-normalized (IFS) metal artifact reduction (MAR) for computed tomography (CT) of hip prostheses. MATERIALS AND METHODS: This study had institutional review board and local ethics committee approval. First, a hip phantom with steel and titanium prostheses that had inlays of water, fat, and contrast media in the pelvis was used to optimize the IFS algorithm. Second, 41 consecutive patients with hip prostheses who were undergoing CT were included. Data sets were reconstructed with filtered back projection, the IFS algorithm, and a linear interpolation MAR algorithm. Two blinded, independent readers evaluated axial, coronal, and sagittal CT reformations for overall image quality, image quality of pelvic organs, and assessment of pelvic abnormalities. CT attenuation and image noise were measured. Statistical analysis included the Friedman test, Wilcoxon signed-rank test, and Levene test. RESULTS: Ex vivo experiments demonstrated an optimized IFS algorithm by using a threshold of 2200 HU with four iterations for both steel and titanium prostheses. Measurements of CT attenuation of the inlays were significantly (P < .001) more accurate for IFS when compared with filtered back projection. In patients, best overall and pelvic organ image quality was found in all reformations with IFS (P < .001). Pelvic abnormalities in 11 of 41 patients (27%) were diagnosed with significantly (P = .002) higher confidence on the basis of IFS images. CT attenuation of bladder (P < .001) and muscle (P = .043) was significantly less variable with IFS compared with filtered back projection and linear interpolation MAR. In comparison with that of FBP and linear interpolation MAR, noise with IFS was similar close to and far from the prosthesis (P = .295). CONCLUSION: The IFS algorithm for CT image reconstruction significantly reduces metal artifacts from hip prostheses, improves the reliability of CT number measurements, and improves the confidence for depicting pelvic abnormalities.
PURPOSE: To assess the value of iterative frequency split-normalized (IFS) metal artifact reduction (MAR) for computed tomography (CT) of hip prostheses. MATERIALS AND METHODS: This study had institutional review board and local ethics committee approval. First, a hip phantom with steel and titanium prostheses that had inlays of water, fat, and contrast media in the pelvis was used to optimize the IFS algorithm. Second, 41 consecutive patients with hip prostheses who were undergoing CT were included. Data sets were reconstructed with filtered back projection, the IFS algorithm, and a linear interpolation MAR algorithm. Two blinded, independent readers evaluated axial, coronal, and sagittal CT reformations for overall image quality, image quality of pelvic organs, and assessment of pelvic abnormalities. CT attenuation and image noise were measured. Statistical analysis included the Friedman test, Wilcoxon signed-rank test, and Levene test. RESULTS: Ex vivo experiments demonstrated an optimized IFS algorithm by using a threshold of 2200 HU with four iterations for both steel and titanium prostheses. Measurements of CT attenuation of the inlays were significantly (P < .001) more accurate for IFS when compared with filtered back projection. In patients, best overall and pelvic organ image quality was found in all reformations with IFS (P < .001). Pelvic abnormalities in 11 of 41 patients (27%) were diagnosed with significantly (P = .002) higher confidence on the basis of IFS images. CT attenuation of bladder (P < .001) and muscle (P = .043) was significantly less variable with IFS compared with filtered back projection and linear interpolation MAR. In comparison with that of FBP and linear interpolation MAR, noise with IFS was similar close to and far from the prosthesis (P = .295). CONCLUSION: The IFS algorithm for CT image reconstruction significantly reduces metal artifacts from hip prostheses, improves the reliability of CT number measurements, and improves the confidence for depicting pelvic abnormalities.
Authors: Wei Zhou; David J Bartlett; Felix E Diehn; Katrina N Glazebrook; Amy L Kotsenas; Rickey E Carter; Joel G Fletcher; Cynthia H McCollough; Shuai Leng Journal: Invest Radiol Date: 2019-04 Impact factor: 6.016
Authors: Martijn F Boomsma; Mireille A Edens; Christiaan P Van Lingen; Niek Warringa; Harmen B Ettema; Cees C P M Verheyen; Mario Maas Journal: Skeletal Radiol Date: 2015-05-06 Impact factor: 2.199
Authors: Sebastian Bickelhaupt; Jana Tesdorff; Frederik Bernd Laun; Tristan Anselm Kuder; Wolfgang Lederer; Susanne Teiner; Klaus Maier-Hein; Heidi Daniel; Anne Stieber; Stefan Delorme; Heinz-Peter Schlemmer Journal: Eur Radiol Date: 2016-05-18 Impact factor: 5.315