title: Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume.
abstract: OBJECTIVE
  The aim of this study was to quantify the effect of overlapping reconstruction on the precision and accuracy of lung nodule volume estimates in a phantom computed tomographic (CT) study.
  
  
  METHODS
  An anthropomorphic phantom was used with a vasculature insert on which synthetic lung nodules were attached. Repeated scans of the phantom were acquired using a 64-slice CT scanner. Overlapping and contiguous reconstructions were performed for a range of CT imaging parameters (exposure, slice thickness, pitch, reconstruction kernel) and a range of nodule characteristics (size, density). Nodule volume was estimated with a previously developed matched-filter algorithm.
  
  
  RESULTS
  Absolute percentage bias across all nodule sizes (n = 2880) was significantly lower when overlapping reconstruction was used, with an absolute percentage bias of 6.6% (95% confidence interval [CI], 6.4-6.9), compared to 13.2% (95% CI, 12.7-13.8) for contiguous reconstruction. Overlapping reconstruction also showed a precision benefit, with a lower standard percentage error of 7.1% (95% CI, 6.9-7.2) compared with 15.3% (95% CI, 14.9-15.7) for contiguous reconstructions across all nodules. Both effects were more pronounced for the smaller, subcentimeter nodules.
  
  
  CONCLUSIONS
  These results support the use of overlapping reconstruction to improve the quantitative assessment of nodule size with CT imaging.
authors.affiliation: Division of Imaging and Applied Mathematics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Avenue, Building 62, Room 4114, Silver Spring, MD 20993, USA. marios.gavrielides@fda.hhs.gov
authors.forename: Marios A
authors.initials: MA
authors.lastname: Gavrielides
authors.forename: Rongping
authors.initials: R
authors.lastname: Zeng
authors.forename: Kyle J
authors.initials: KJ
authors.lastname: Myers
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Nicholas
authors.initials: N
authors.lastname: Petrick
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging / Q000473:pathology*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*; D047368:Tumor Burden