Optimal Virtual Monochromatic Images for Evaluation of Normal Tissues and Head and Neck Cancer Using Dual-Energy CT

AJNR Am J Neuroradiol. 2015 Aug;36(8):1518-24. doi: 10.3174/ajnr.A4314. Epub 2015 May 28.

Abstract

Background and purpose: Dual-energy CT is not used routinely for evaluation of the head and neck, and there is no consensus on the optimal virtual monochromatic image energies for evaluating normal tissues or head and neck cancer. We performed a quantitative evaluation to determine the optimal virtual monochromatic images for visualization of normal tissues, head and neck squamous cell carcinoma, and lymphadenopathy.

Materials and methods: Dual-energy CT scans from 10 healthy patients and 30 patients with squamous cell carcinoma were evaluated at different virtual monochromatic energy levels ranging from 40 to 140 keV. The signal-to-noise ratios of muscles at 6 different levels, glands (parotid, sublingual, submandibular, and thyroid), 30 tumors, and 17 metastatic lymph nodes were determined as measures of optimal image quality. Lesion attenuation and contrast-to-noise ratios (compared with those of muscle) were evaluated to assess lesion conspicuity.

Results: The optimal signal-to-noise ratio for all the tissues was at 65 keV (P < .0001). However, tumor attenuation (P < .0001), attenuation difference between tumor and muscles (P = .03), and lesion contrast-to-noise ratios (P < .0001) were highest at 40 keV.

Conclusions: The optimal image signal-to-noise ratio is at 65 keV, but tumor conspicuity compared with that of muscle is greatest at 40 keV. Optimal evaluation of the neck may be best achieved by a multiparametric approach, with 65-keV virtual monochromatic images providing the best overall image quality and targeted use of 40-keV virtual monochromatic images for tumor evaluation.

MeSH terms

  • Adult
  • Aged
  • Carcinoma, Squamous Cell / diagnostic imaging*
  • Female
  • Head and Neck Neoplasms / diagnostic imaging*
  • Humans
  • Male
  • Middle Aged
  • Radiographic Image Interpretation, Computer-Assisted / methods
  • Sensitivity and Specificity
  • Signal-To-Noise Ratio
  • Tomography, X-Ray Computed / methods*