PT - JOURNAL ARTICLE AU - S. Gaddikeri AU - J.B. Andre AU - J. Benjert AU - D.S. Hippe AU - Y. Anzai TI - Impact of Model-Based Iterative Reconstruction on Image Quality of Contrast-Enhanced Neck CT AID - 10.3174/ajnr.A4123 DP - 2014 Oct 09 TA - American Journal of Neuroradiology 4099 - http://www.ajnr.org/content/early/2014/10/09/ajnr.A4123.short 4100 - http://www.ajnr.org/content/early/2014/10/09/ajnr.A4123.full 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 < .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 ASiR3030% adaptive statistical iterative reconstructionBNbackground noiseCNRcontrast-to-noise ratioFBPfiltered back-projectionHUHounsfield unitsMBIRmodel-based iterative reconstructionPMpectoris muscleSCMsternocleidomastoid muscleSVCsuperior vena cava