OBJECTIVES: Dual-energy computed tomographic angiography (DE-CTA) has been demonstrated to improve the visualization of the head and neck vessels. The aim of this study was to test the potential of split-filter single-source dual-energy CT to automatically remove bone from the final CTA data set. MATERIALS AND METHODS: Dual-energy CTA was performed in 50 consecutive patients to evaluate the supra-aortic arteries, either to grade carotid artery stenosis or to rule out traumatic dissections. Dual-energy CTA was performed on a 128-slice single-source CT system equipped with a special filter array to separate the 120-kV spectrum into a high- and a low-energy spectrum for DE-based automated bone removal. Image quality of fully automated bone suppression and subsequent manual optimization was evaluated by 2 radiologists on maximum intensity projections using a 4-grade scoring system. The effect of image reconstruction with an iterative metal artifact reduction algorithm on DE postprocessing was tested using a 3-grade scoring system, and the time demand for each postprocessing step was measured. RESULTS: Two patients were excluded due to insufficient arterial contrast enhancement; in the remaining 48 patients, automated bone removal could be performed successfully. The addition of iterative metal artifact reduction algorithm improved image quality in 58.3% of the cases. After manual optimization, DE-CTA image quality was rated excellent in 7, good in 29, and moderate in 10 patients. Interobserver agreement was high (κ = 0.85). Stenosis grading was not influenced using DE-CTA with bone removal as compared with the original CTA. The time demand for DE image reconstruction was significantly higher than for single-energy reconstruction (42.1 vs 20.9 seconds). CONCLUSIONS: Our results suggest that bone removal in DE-CTA of the head and neck vessels with a single-source CT is feasible and can be performed within acceptable time and moderate user interaction.
OBJECTIVES: Dual-energy computed tomographic angiography (DE-CTA) has been demonstrated to improve the visualization of the head and neck vessels. The aim of this study was to test the potential of split-filter single-source dual-energy CT to automatically remove bone from the final CTA data set. MATERIALS AND METHODS: Dual-energy CTA was performed in 50 consecutive patients to evaluate the supra-aortic arteries, either to grade carotid artery stenosis or to rule out traumatic dissections. Dual-energy CTA was performed on a 128-slice single-source CT system equipped with a special filter array to separate the 120-kV spectrum into a high- and a low-energy spectrum for DE-based automated bone removal. Image quality of fully automated bone suppression and subsequent manual optimization was evaluated by 2 radiologists on maximum intensity projections using a 4-grade scoring system. The effect of image reconstruction with an iterative metal artifact reduction algorithm on DE postprocessing was tested using a 3-grade scoring system, and the time demand for each postprocessing step was measured. RESULTS: Two patients were excluded due to insufficient arterial contrast enhancement; in the remaining 48 patients, automated bone removal could be performed successfully. The addition of iterative metal artifact reduction algorithm improved image quality in 58.3% of the cases. After manual optimization, DE-CTA image quality was rated excellent in 7, good in 29, and moderate in 10 patients. Interobserver agreement was high (κ = 0.85). Stenosis grading was not influenced using DE-CTA with bone removal as compared with the original CTA. The time demand for DE image reconstruction was significantly higher than for single-energy reconstruction (42.1 vs 20.9 seconds). CONCLUSIONS: Our results suggest that bone removal in DE-CTA of the head and neck vessels with a single-source CT is feasible and can be performed within acceptable time and moderate user interaction.
Authors: Jack W Lambert; Yuxin Sun; Robert G Gould; Michael A Ohliger; Zhixi Li; Benjamin M Yeh Journal: Invest Radiol Date: 2017-04 Impact factor: 6.016
Authors: Matthias Stefan May; Marco Wiesmueller; Rafael Heiss; Michael Brand; Joscha Bruegel; Michael Uder; Wolfgang Wuest Journal: Eur Radiol Date: 2018-10-18 Impact factor: 5.315
Authors: Boyuan Li; Derrek Spronk; Yueting Luo; Connor Puett; Christina R Inscoe; Donald A Tyndall; Yueh Z Lee; Jianping Lu; Otto Zhou Journal: PLoS One Date: 2022-02-03 Impact factor: 3.240