title: Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features.
abstract: OBJECTIVE
  The purpose of this study was to develop a novel algorithm to create a robust internal respiratory signal (IRS) for retrospective sorting of four-dimensional (4D) computed tomography (CT) images.
  
  
  METHODS
  The proposed algorithm combines information from the Fourier transform of the CT images and from internal anatomical features to form the IRS. The algorithm first extracts potential respiratory signals from low-frequency components in the Fourier space and selected anatomical features in the image space. A clustering algorithm then constructs groups of potential respiratory signals with similar temporal oscillation patterns. The clustered group with the largest number of similar signals is chosen to form the final IRS. To evaluate the performance of the proposed algorithm, the IRS was computed and compared with the external respiratory signal from the real-time position management (RPM) system on 80 patients.
  
  
  RESULTS
  In 72 (90%) of the 4D CT data sets tested, the IRS computed by the authors' proposed algorithm matched with the RPM signal based on their normalized cross correlation. For these data sets with matching respiratory signals, the average difference between the end inspiration times (Δtins) in the IRS and RPM signal was 0.11 s, and only 2.1% of Δtins were more than 0.5 s apart. In the eight (10%) 4D CT data sets in which the IRS and the RPM signal did not match, the average Δtins was 0.73 s in the nonmatching couch positions, and 35.4% of them had a Δtins greater than 0.5 s. At couch positions in which IRS did not match the RPM signal, a correlation-based metric indicated poorer matching of neighboring couch positions in the RPM-sorted images. This implied that, when IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal.
  
  
  CONCLUSIONS
  The authors' proposed algorithm can generate robust IRSs that can be used for retrospective sorting of 4D CT data. The algorithm is completely automatic and requires very little processing time. The algorithm is cost efficient and can be easily adopted for everyday clinical use.
authors.affiliation: Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors.forename: Cheukkai
authors.initials: C
authors.lastname: Hui
authors.affiliation: Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors.forename: Yelin
authors.initials: Y
authors.lastname: Suh
authors.affiliation: Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and Department of Radiation Physics, The University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030.
authors.forename: Daniel
authors.initials: D
authors.lastname: Robertson
authors.affiliation: Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and Department of Imaging Physics, The University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030.
authors.forename: Tinsu
authors.initials: T
authors.lastname: Pan
authors.affiliation: Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors.forename: Prajnan
authors.initials: P
authors.lastname: Das
authors.affiliation: Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors.forename: Christopher H
authors.initials: CH
authors.lastname: Crane
authors.affiliation: Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and Department of Radiation Physics, The University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030.
authors.forename: Sam
authors.initials: S
authors.lastname: Beddar
mesh_terms: D000465:Algorithms; D016477:Artifacts; D066264:Datasets as Topic; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D009038:Motion; D011860:Radiography, Abdominal / Q000379:methods; D013902:Radiography, Thoracic / Q000379:methods; D012119:Respiration; D012189:Retrospective Studies; D013997:Time Factors