title: Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT.
abstract: OBJECTIVE
  To optimize the slice thickness/overlap parameters for image reconstruction and to study the effect of iterative reconstruction (IR) on detectability and characterization of small non-calcified pulmonary nodules during low-dose thoracic CT.
  
  
  METHODS
  Data was obtained from computer simulations, phantom, and patient CTs. Simulations and phantom CTs were performed with 9 nodules (5, 8, and 10 mm with 100, -630, and -800 HU). Patient data were based on 11 ground glass opacities (GGO) and 9 solid nodules. For each analysis the nodules were reconstructed with filtered back projection and IR algorithms using 10 different combinations of slice thickness/overlap (0.5-5 mm). The attenuation (CT#) and the contrast to noise ratio (CNR) were measured. Spearman's coefficient was used to correlate the error in CT# measurements and slice thickness. Paired Student's t test was used to measure the significance of the errors.
  
  
  RESULTS
  CNR measurements: CNR increases with increasing slice thickness/overlap for large nodules and peaks at 4.0/2.0 mm for smaller ones. Use of IR increases the CNR of GGOs by 60 %. CT# measurements: Increasing slice thickness/overlap above 3.0/1.5 mm results in decreased CT# measurement accuracy.
  
  
  CONCLUSIONS
  Optimal detection of small pulmonary nodules requires slice thickness/overlap of 4.0/2.0 mm. Slice thickness/overlap of 2.0/2.0 mm is required for optimal nodule characterization. IR improves conspicuity of small ground glass nodules through a significant increase in nodule CNR.
  
  
  CONCLUSIONS
  • Slice thickness/overlap affects the accuracy of pulmonary nodule detection and characterization. • Slice thickness ≥3 mm increases the risk of misclassifying small nodules. • Optimal nodule detection during low-dose CT requires 4.0/2.0-mm reconstructions. • Optimal nodule characterization during low-dose CT requires 2.0/2.0-mm reconstructions. • Iterative reconstruction improves the CNR of ground glass nodules by 60 %.
authors.affiliation: Institute of Biomaterial and Biomedical Engineering, University of Toronto, Room RS-420A, 164 College Street, Toronto, ON, Canada, M5S 3G9.
authors.forename: SayedMasoud
authors.initials: S
authors.lastname: Hashemi
authors.forename: Hatem
authors.initials: H
authors.lastname: Mehrez
authors.forename: Richard S C
authors.initials: RS
authors.lastname: Cobbold
authors.forename: Narinder S
authors.initials: NS
authors.lastname: Paul
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D055613:Multiple Pulmonary Nodules / Q000000981:diagnostic imaging*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*