title: Evaluation of tumor localization in respiration motion-corrected cone-beam CT: prospective study in lung.
abstract: OBJECTIVE
  Target localization accuracy of cone-beam CT (CBCT) images used in radiation treatment of respiratory disease sites is affected by motion artifacts (blurring and streaking). The authors have previously reported on a method of respiratory motion correction in thoracic CBCT at end expiration (EE). The previous retrospective study was limited to examination of reducing motion artifacts in a small number of patient cases. They report here on a prospective study in a larger group of lung cancer patients to evaluate respiratory motion-corrected (RMC)-CBCT ability to improve lung tumor localization accuracy and reduce motion artifacts in Linac-mounted CBCT images. A second study goal examines whether the motion correction derived from a respiration-correlated CT (RCCT) at simulation yields similar tumor localization accuracy at treatment.
  
  
  METHODS
  In an IRB-approved study, 19 lung cancer patients (22 tumors) received a RCCT at simulation, and on one treatment day received a RCCT, a respiratory-gated CBCT at end expiration, and a 1-min CBCT. A respiration monitor of abdominal displacement was used during all scans. In addition to a CBCT reconstruction without motion correction, the motion correction method was applied to the same 1-min scan. Projection images were sorted into ten bins based on abdominal displacement, and each bin was reconstructed to produce ten intermediate CBCT images. Each intermediate CBCT was deformed to the end expiration state using a motion model derived from RCCT. The deformed intermediate CBCT images were then added to produce a final RMC-CBCT. In order to evaluate the second study goal, the CBCT was corrected in two ways, one using a model derived from the RCCT at simulation [RMC-CBCT(sim)], the other from the RCCT at treatment [RMC-CBCT(tx)]. Image evaluation compared uncorrected CBCT, RMC-CBCT(sim), and RMC-CBCT(tx). The gated CBCT at end expiration served as the criterion standard for comparison. Using automatic rigid image registration, each CBCT was registered twice to the gated CBCT, first aligned to spine, second to tumor in lung. Localization discrepancy was defined as the difference between tumor and spine registration. Agreement in tumor localization with the gated CBCT was further evaluated by calculating a normalized cross correlation (NCC) of pixel intensities within a volume-of-interest enclosing the tumor in lung.
  
  
  RESULTS
  Tumor localization discrepancy was reduced with RMC-CBCT(tx) in 17 out of 22 cases relative to no correction. If one considers cases in which tumor motion is 5 mm or more in the RCCT, tumor localization discrepancy is reduced with RMC-CBCT(tx) in 14 out of 17 cases (p = 0.04), and with RMC-CBCT(sim) in 13 out of 17 cases (p = 0.05). Differences in localization discrepancy between correction models [RMC-CBCT(sim) vs RMC-CBCT(tx)] were less than 2 mm. In 21 out of 22 cases, improvement in NCC was higher with RMC-CBCT(tx) relative to no correction (p < 0.0001). Differences in NCC between RMC-CBCT(sim) and RMC-CBCT(tx) were small.
  
  
  CONCLUSIONS
  Motion-corrected CBCT improves lung tumor localization accuracy and reduces motion artifacts in nearly all cases. Motion correction at end expiration using RCCT acquired at simulation yields similar results to that using a RCCT on the treatment day (2-3 weeks after simulation).
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Oleksandr
authors.initials: O
authors.lastname: Dzyubak
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Russell
authors.initials: R
authors.lastname: Kincaid
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Agung
authors.initials: A
authors.lastname: Hertanto
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Yu-Chi
authors.initials: YC
authors.lastname: Hu
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Hai
authors.initials: H
authors.lastname: Pham
authors.affiliation: Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Andreas
authors.initials: A
authors.lastname: Rimner
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Ellen
authors.initials: E
authors.lastname: Yorke
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Qinghui
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065.
authors.forename: Gig S
authors.initials: GS
authors.lastname: Mageras
mesh_terms: D000005:Abdomen / Q000503:physiopathology; D016477:Artifacts; D003198:Computer Simulation; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D003964:Diaphragm / Q000000981:diagnostic imaging / Q000503:physiopathology; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging* / Q000503:physiopathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000503:physiopathology / Q000532:radiotherapy; D008954:Models, Biological; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods; D011446:Prospective Studies; D012119:Respiration; D013131:Spine / Q000000981:diagnostic imaging