title: Accuracy of mandibular cross-sectional imaging with tuned- aperture computed tomography (TACT), iteratively reconstructed TACT, and multidirectional, linear, and transverse panoramic tomography.
abstract: OBJECTIVE
  This study was designed to compare 5 modalities with respect to accuracy in mandibular cross-sectional imaging. The modalities tested were tuned-aperture computed tomography (TACT), iteratively reconstructed TACT, multidirectional tomography, linear tomography, and transverse panoramic tomography.
  
  
  METHODS
  Twenty sites were selected from 3 dry human mandibles, and cross-sectional views were imaged through use of each of the 5 modalities. A quantitative analysis included measurements of 2 linear distances; a qualitative study included image evaluation by 6 observers. A nested mixed analysis of variance model was used to control for mandibles and locations within mandibles for the quantitative analysis; the Cochran-Mantel-Haenszel test was used for the qualitative analysis.
  
  
  RESULTS
  There was a significant difference in measurement error for maximum height but not for width. There was also a significant difference in qualitative image evaluation results.
  
  
  CONCLUSIONS
  Of the 5 modalities tested, the narrow-layer multidirectional tomographic technique produced the greatest diagnostic accuracy and quality in cross-sectional imaging. The transverse panoramic tomographic technique produced the least diagnostic accuracy and quality. Linear tomography, TACT, and iteratively reconstructed TACT were intermediate in accuracy and quality.
authors.affiliation: Department of Diagnostic Sciences, Baylor College of Dentistry, Dallas, TX 75266-0677, USA. hliang@tambcd.edu
authors.forename: H
authors.initials: H
authors.lastname: Liang
authors.forename: D A
authors.initials: DA
authors.lastname: Tyndall
authors.forename: J B
authors.initials: JB
authors.lastname: Ludlow
authors.forename: L A
authors.initials: LA
authors.lastname: Lang
authors.forename: M E
authors.initials: ME
authors.lastname: Nunn
mesh_terms: D000704:Analysis of Variance; D019412:Anatomy, Cross-Sectional; D002508:Cephalometry; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007575:Jaw, Edentulous / Q000000981:diagnostic imaging; D008334:Mandible / Q000000981:diagnostic imaging*; D015588:Observer Variation; D011856:Radiographic Image Enhancement / Q000379:methods; D011862:Radiography, Panoramic / Q000379:methods*; D018709:Statistics, Nonparametric; D014056:Tomography, X-Ray / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*