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Research ArticleHEAD & NECK

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

S. Gaddikeri, J.B. Andre, J. Benjert, D.S. Hippe and Y. Anzai
American Journal of Neuroradiology October 2014, DOI: https://doi.org/10.3174/ajnr.A4123
S. Gaddikeri
From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington; Department of Neuroradiology (J.B.), University of Washington and VA Puget Sound, Seattle, Washington; and Department of Radiology (D.S.H.), University of Washington, Seattle, Washington.
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J.B. Andre
From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington; Department of Neuroradiology (J.B.), University of Washington and VA Puget Sound, Seattle, Washington; and Department of Radiology (D.S.H.), University of Washington, Seattle, Washington.
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J. Benjert
From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington; Department of Neuroradiology (J.B.), University of Washington and VA Puget Sound, Seattle, Washington; and Department of Radiology (D.S.H.), University of Washington, Seattle, Washington.
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D.S. Hippe
From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington; Department of Neuroradiology (J.B.), University of Washington and VA Puget Sound, Seattle, Washington; and Department of Radiology (D.S.H.), University of Washington, Seattle, Washington.
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Y. Anzai
From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington; Department of Neuroradiology (J.B.), University of Washington and VA Puget Sound, Seattle, Washington; and Department of Radiology (D.S.H.), University of Washington, Seattle, Washington.
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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
  • © 2015 American Society of Neuroradiology
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Impact of Model-Based Iterative Reconstruction on Image Quality of Contrast-Enhanced Neck CT
S. Gaddikeri, J.B. Andre, J. Benjert, D.S. Hippe, Y. Anzai
American Journal of Neuroradiology Oct 2014, DOI: 10.3174/ajnr.A4123

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Impact of Model-Based Iterative Reconstruction on Image Quality of Contrast-Enhanced Neck CT
S. Gaddikeri, J.B. Andre, J. Benjert, D.S. Hippe, Y. Anzai
American Journal of Neuroradiology Oct 2014, DOI: 10.3174/ajnr.A4123
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