title: Semiautomated motion correction of tumors in lung CT-perfusion studies.
abstract: OBJECTIVE
  To compare the relative performance of one-dimensional (1D) manual, rigid-translational, and nonrigid registration techniques to correct misalignment of lung tumor anatomy acquired from computed tomography perfusion (CTp) datasets.
  
  
  METHODS
  Twenty-five datasets in patients with lung tumors who had undergone a CTp protocol were evaluated. Each dataset consisted of one reference CT image from an initial cine slab and six subsequent breathhold helical volumes (16-row multi-detector CT), acquired during intravenous contrast administration. Each helical volume was registered to the reference image using two semiautomated intensity-based registration methods (rigid-translational and nonrigid), and 1D manual registration (the only registration method available in the relevant application software). The performance of each technique to align tumor regions was assessed quantitatively (percent overlap and distance of center of mass), and by a visual validation study (using a 5-point scale). The registration methods were statistically compared using linear mixed and ordinal probit regression models.
  
  
  RESULTS
  Quantitatively, tumor alignment with the nonrigid method compared to rigid-translation was borderline significant, which in turn was significantly better than the 1D manual method: average (± SD) percent overlap, 91.8 ± 2.3%, 87.7 ± 5.5%, and 77.6 ± 5.9%, respectively; and average (± SD) DCOM, 0.41 ± 0.16 mm, 1.08 ± 1.13 mm, and 2.99 ± 2.93 mm, respectively (all P < .0001). Visual validation confirmed these findings.
  
  
  CONCLUSIONS
  Semiautomated registration methods achieved superior alignment of lung tumors compared to the 1D manual method. This will hopefully translate into more reliable CTp analyses.
authors.affiliation: Department of Imaging Physics, MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Chandler
authors.forename: Wei
authors.initials: W
authors.lastname: Wei
authors.forename: Delise H
authors.initials: DH
authors.lastname: Herron
authors.forename: Ella F
authors.initials: EF
authors.lastname: Anderson
authors.forename: Valen E
authors.initials: VE
authors.lastname: Johnson
authors.forename: Chaan S
authors.initials: CS
authors.lastname: Ng
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016477:Artifacts; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D009038:Motion; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D055420:Perfusion Imaging / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult