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Impact of Model-Based Iterative Reconstruction on Image Quality of Contrast-Enhanced Neck CT

Impact of Model-Based Iterative Reconstruction on Image Quality of Contrast-Enhanced Neck CT BACKGROUND AND PURPOSE: Improved image quality is clinically desired for contrast-enhanced CT of the neck. We compared 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction algorithms for the assessment of image quality of contrast-enhanced CT of the neck. MATERIALS AND METHODS: Neck contrast-enhanced CT data from 64 consecutive patients were reconstructed retrospectively by using 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction. Objective image quality was assessed by comparing SNR, contrast-to-noise ratio, and background noise at levels 1 (mandible) and 2 (superior mediastinum). Two independent blinded readers subjectively graded the image quality on a scale of 1–5, (grade 5 = excellent image quality without artifacts and grade 1 = nondiagnostic image quality with significant artifacts). The percentage of agreement and disagreement between the 2 readers was assessed. RESULTS: Compared with 30% adaptive statistical iterative reconstruction, model-based iterative reconstruction significantly improved the SNR and contrast-to-noise ratio at levels 1 and 2. Model-based iterative reconstruction also decreased background noise at level 1 ( P = .016), though there was no difference at level 2 ( P = .61). Model-based iterative reconstruction was scored higher than 30% adaptive statistical iterative reconstruction by both reviewers at the nasopharynx ( P < .001) and oropharynx ( P < .001) and for overall image quality ( P < .001) and was scored lower at the vocal cords ( P < .001) and sternoclavicular junction ( P < .001), due to artifacts related to thyroid shielding that were specific for model-based iterative reconstruction. CONCLUSIONS: Model-based iterative reconstruction offers improved subjective and objective image quality as evidenced by a higher SNR and contrast-to-noise ratio and lower background noise within the same dataset for contrast-enhanced neck CT. Model-based iterative reconstruction has the potential to reduce the radiation dose while maintaining the image quality, with a minor downside being prominent artifacts related to thyroid shield use on model-based iterative reconstruction. ABBREVIATIONS: ASiR30 30% adaptive statistical iterative reconstruction BN background noise CNR contrast-to-noise ratio FBP filtered back-projection HU Hounsfield units MBIR model-based iterative reconstruction PM pectoris muscle SCM sternocleidomastoid muscle SVC superior vena cava http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Neuroradiology American Journal of Neuroradiology

Impact of Model-Based Iterative Reconstruction on Image Quality of Contrast-Enhanced Neck CT

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Publisher
American Journal of Neuroradiology
Copyright
Copyright © 2015 by the American Society of Neuroradiology.
ISSN
0195-6108
eISSN
1936-959X
DOI
10.3174/ajnr.A4123
pmid
25300982
Publisher site
See Article on Publisher Site

Abstract

BACKGROUND AND PURPOSE: Improved image quality is clinically desired for contrast-enhanced CT of the neck. We compared 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction algorithms for the assessment of image quality of contrast-enhanced CT of the neck. MATERIALS AND METHODS: Neck contrast-enhanced CT data from 64 consecutive patients were reconstructed retrospectively by using 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction. Objective image quality was assessed by comparing SNR, contrast-to-noise ratio, and background noise at levels 1 (mandible) and 2 (superior mediastinum). Two independent blinded readers subjectively graded the image quality on a scale of 1–5, (grade 5 = excellent image quality without artifacts and grade 1 = nondiagnostic image quality with significant artifacts). The percentage of agreement and disagreement between the 2 readers was assessed. RESULTS: Compared with 30% adaptive statistical iterative reconstruction, model-based iterative reconstruction significantly improved the SNR and contrast-to-noise ratio at levels 1 and 2. Model-based iterative reconstruction also decreased background noise at level 1 ( P = .016), though there was no difference at level 2 ( P = .61). Model-based iterative reconstruction was scored higher than 30% adaptive statistical iterative reconstruction by both reviewers at the nasopharynx ( P < .001) and oropharynx ( P < .001) and for overall image quality ( P < .001) and was scored lower at the vocal cords ( P < .001) and sternoclavicular junction ( P < .001), due to artifacts related to thyroid shielding that were specific for model-based iterative reconstruction. CONCLUSIONS: Model-based iterative reconstruction offers improved subjective and objective image quality as evidenced by a higher SNR and contrast-to-noise ratio and lower background noise within the same dataset for contrast-enhanced neck CT. Model-based iterative reconstruction has the potential to reduce the radiation dose while maintaining the image quality, with a minor downside being prominent artifacts related to thyroid shield use on model-based iterative reconstruction. ABBREVIATIONS: ASiR30 30% adaptive statistical iterative reconstruction BN background noise CNR contrast-to-noise ratio FBP filtered back-projection HU Hounsfield units MBIR model-based iterative reconstruction PM pectoris muscle SCM sternocleidomastoid muscle SVC superior vena cava

Journal

American Journal of NeuroradiologyAmerican Journal of Neuroradiology

Published: Feb 1, 2015

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