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PT  - JOURNAL ARTICLE
AU  - Gaddikeri, S.
AU  - Andre, J.B.
AU  - Benjert, J.
AU  - Hippe, D.S.
AU  - Anzai, Y.
TI  - Impact of Model-Based Iterative Reconstruction on Image Quality of Contrast-Enhanced Neck CT
AID  - 10.3174/ajnr.A4123
DP  - 2015 Feb 01
TA  - American Journal of Neuroradiology
PG  - 391--396
VI  - 36
IP  - 2
4099  - http://www.ajnr.org/content/36/2/391.short
4100  - http://www.ajnr.org/content/36/2/391.full
SO  - Am. J. Neuroradiol.2015 Feb 01; 36
AB  - 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 &amp;lt; .001) and oropharynx (P &amp;lt; .001) and for overall image quality (P &amp;lt; .001) and was scored lower at the vocal cords (P &amp;lt; .001) and sternoclavicular junction (P &amp;lt; .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. ASiR3030% adaptive statistical iterative reconstructionBNbackground noiseCNRcontrast-to-noise ratioFBPfiltered back-projectionHUHounsfield unitsMBIRmodel-based iterative reconstructionPMpectoris muscleSCMsternocleidomastoid muscleSVCsuperior vena cava