title: Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions.
abstract: A pulmonary ventilation imaging technique based on four-dimensional (4D) computed tomography (CT) has advantages over existing techniques. However, physiologically accurate 4D-CT ventilation imaging has not been achieved in patients. The purpose of this study was to evaluate 4D-CT ventilation imaging by correlating ventilation with emphysema. Emphysematous lung regions are less ventilated and can be used as surrogates for low ventilation. We tested the hypothesis: 4D-CT ventilation in emphysematous lung regions is significantly lower than in non-emphysematous regions. Four-dimensional CT ventilation images were created for 12 patients with emphysematous lung regions as observed on CT, using a total of four combinations of two deformable image registration (DIR) algorithms: surface-based (DIR(sur)) and volumetric (DIR(vol)), and two metrics: Hounsfield unit (HU) change (V(HU)) and Jacobian determinant of deformation (V(Jac)), yielding four ventilation image sets per patient. Emphysematous lung regions were detected by density masking. We tested our hypothesis using the one-tailed t-test. Visually, different DIR algorithms and metrics yielded spatially variant 4D-CT ventilation images. The mean ventilation values in emphysematous lung regions were consistently lower than in non-emphysematous regions for all the combinations of DIR algorithms and metrics. V(HU) resulted in statistically significant differences for both DIR(sur) (0.14 ± 0.14 versus 0.29 ± 0.16, p = 0.01) and DIR(vol) (0.13 ± 0.13 versus 0.27 ± 0.15, p < 0.01). However, V(Jac) resulted in non-significant differences for both DIR(sur) (0.15 ± 0.07 versus 0.17 ± 0.08, p = 0.20) and DIR(vol) (0.17 ± 0.08 versus 0.19 ± 0.09, p = 0.30). This study demonstrated the strong correlation between the HU-based 4D-CT ventilation and emphysema, which indicates the potential for HU-based 4D-CT ventilation imaging to achieve high physiologic accuracy. A further study is needed to confirm these results.
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Dr, Stanford, CA 94305-5847, USA. Tokihiro@stanford.edu
authors.forename: Tokihiro
authors.initials: T
authors.lastname: Yamamoto
authors.forename: Sven
authors.initials: S
authors.lastname: Kabus
authors.forename: Tobias
authors.initials: T
authors.lastname: Klinder
authors.forename: Cristian
authors.initials: C
authors.lastname: Lorenz
authors.forename: Jens
authors.initials: J
authors.lastname: von Berg
authors.forename: Thomas
authors.initials: T
authors.lastname: Blaffert
authors.forename: Billy W
authors.initials: BW
authors.lastname: Loo
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Keall
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015331:Cohort Studies; D005260:Female; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D011656:Pulmonary Emphysema / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D012123:Pulmonary Ventilation; D013990:Tidal Volume