Image noise and liver lesion detection with MDCT: a phantom study

AJR Am J Roentgenol. 2011 Aug;197(2):437-41. doi: 10.2214/AJR.10.5726.

Abstract

Objective: The purpose of this study was to determine the upper limit of noise for detection of small low-contrast lesions in a liver phantom.

Materials and methods: A CT liver phantom containing 21 low-contrast, low-attenuation, circular simulated lesions ranging in size from 2.4 to 10 mm was scanned 23 times at different tube current ranges (varying noise index) on a 64-MDCT scanner with automatic tube current modulation. The attenuation of the simulated lesions was 20 HU less than that of the liver-equivalent background. Three radiologists independently reviewed the resultant CT images, which contained either a low-contrast lesion or no lesion and scored certainty of lesion detection using a 4-point Likert scale. Overall performance was evaluated by sensitivity analysis with receiver operator curve and area under the curve (A(z)) computation for ranges of noise index.

Results: The reviewers achieved 100% sensitivity with a noise index of 15 or less for lesions measuring 6.3-10.0 mm (A(z) = 0.96). Increasing noise index to the 17-21 range resulted in a minor decrease in sensitivity and overall performance (sensitivity, 92.3%; A(z) = 0.93). A further increase in noise index to the 23-27 range resulted in a moderate decrease in sensitivity (sensitivity, 81.4%; A(z) = 0.77). Beyond the noise index 23-27 range, sensitivity dropped markedly from 81.4% to 39%. Agreement between the three readers in assessing the image sets was moderate.

Conclusion: For detection of small low-contrast lesions in the liver phantom model used in this study, the upper limit of noise index may be in the 15-21 range for sensitivity greater than 90%.

MeSH terms

  • Algorithms
  • Area Under Curve
  • Humans
  • Liver Neoplasms / diagnostic imaging*
  • Phantoms, Imaging*
  • ROC Curve
  • Radiographic Image Interpretation, Computer-Assisted
  • Sensitivity and Specificity
  • Tomography, Spiral Computed / methods*