title: Optimal Virtual Monochromatic Images for Evaluation of Normal Tissues and Head and Neck Cancer Using Dual-Energy CT.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: From the Department of Radiology (S.L., M.L., R.F.), Jewish General Hospital, McGill University, Montreal, Quebec, Canada.
authors.forename: S
authors.initials: S
authors.lastname: Lam
authors.affiliation: Department of Radiology (R.G.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
authors.forename: R
authors.initials: R
authors.lastname: Gupta
authors.affiliation: From the Department of Radiology (S.L., M.L., R.F.), Jewish General Hospital, McGill University, Montreal, Quebec, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Levental
authors.affiliation: Joint Department of Medical Imaging (E.Y.), Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.
authors.forename: E
authors.initials: E
authors.lastname: Yu
authors.affiliation: Department of Radiology (H.D.C.), Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts.
authors.forename: H D
authors.initials: HD
authors.lastname: Curtin
authors.affiliation: From the Department of Radiology (S.L., M.L., R.F.), Jewish General Hospital, McGill University, Montreal, Quebec, Canada rforghani@jgh.mcgill.ca.
authors.forename: R
authors.initials: R
authors.lastname: Forghani
mesh_terms: D000328:Adult; D000368:Aged; D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging*; D005260:Female; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*