title: Single-energy computed tomography-based pulmonary perfusion imaging: Proof-of-principle in a canine model.
abstract: OBJECTIVE
  Radiotherapy (RT) that selectively avoids irradiating highly functional lung regions may reduce pulmonary toxicity, which is substantial in lung cancer RT. Single-energy computed tomography (CT) pulmonary perfusion imaging has several advantages (e.g., higher resolution) over other modalities and has great potential for widespread clinical implementation, particularly in RT. The purpose of this study was to establish proof-of-principle for single-energy CT perfusion imaging.
  
  
  METHODS
  Single-energy CT perfusion imaging is based on the following: (1) acquisition of end-inspiratory breath-hold CT scans before and after intravenous injection of iodinated contrast agents, (2) deformable image registration (DIR) for spatial mapping of those two CT image data sets, and (3) subtraction of the precontrast image data set from the postcontrast image data set, yielding a map of regional Hounsfield unit (HU) enhancement, a surrogate for regional perfusion. In a protocol approved by the institutional animal care and use committee, the authors acquired CT scans in the prone position for a total of 14 anesthetized canines (seven canines with normal lungs and seven canines with diseased lungs). The elastix algorithm was used for DIR. The accuracy of DIR was evaluated based on the target registration error (TRE) of 50 anatomic pulmonary landmarks per subject for 10 randomly selected subjects as well as on singularities (i.e., regions where the displacement vector field is not bijective). Prior to perfusion computation, HUs of the precontrast end-inspiratory image were corrected for variation in the lung inflation level between the precontrast and postcontrast end-inspiratory CT scans, using a model built from two additional precontrast CT scans at end-expiration and midinspiration. The authors also assessed spatial heterogeneity and gravitationally directed gradients of regional perfusion for normal lung subjects and diseased lung subjects using a two-sample two-tailed t-test.
  
  
  RESULTS
  The mean TRE (and standard deviation) was 0.6 ± 0.7 mm (smaller than the voxel dimension) for DIR between pre contrast and postcontrast end-inspiratory CT image data sets. No singularities were observed in the displacement vector fields. The mean HU enhancement (and standard deviation) was 37.3 ± 10.5 HU for normal lung subjects and 30.7 ± 13.5 HU for diseased lung subjects. Spatial heterogeneity of regional perfusion was found to be higher for diseased lung subjects than for normal lung subjects, i.e., a mean coefficient of variation of 2.06 vs 1.59 (p = 0.07). The average gravitationally directed gradient was strong and significant (R(2) = 0.99, p < 0.01) for normal lung dogs, whereas it was moderate and nonsignificant (R(2) = 0.61, p = 0.12) for diseased lung dogs.
  
  
  CONCLUSIONS
  This canine study demonstrated the accuracy of DIR with subvoxel TREs on average, higher spatial heterogeneity of regional perfusion for diseased lung subjects than for normal lung subjects, and a strong gravitationally directed gradient for normal lung subjects, providing proof-of-principle for single-energy CT pulmonary perfusion imaging. Further studies such as comparison with other perfusion imaging modalities will be necessary to validate the physiological significance.
authors.affiliation: Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California 95817.
authors.forename: Tokihiro
authors.initials: T
authors.lastname: Yamamoto
authors.affiliation: Department of Surgical and Radiological Sciences, University of California Davis School of Veterinary Medicine, Davis, California 95616.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Kent
authors.affiliation: Department of Surgical and Radiological Sciences, University of California Davis School of Veterinary Medicine, Davis, California 95616.
authors.forename: Erik R
authors.initials: ER
authors.lastname: Wisner
authors.affiliation: Department of Medicine and Epidemiology, University of California Davis School of Veterinary Medicine, Davis, California 95616.
authors.forename: Lynelle R
authors.initials: LR
authors.lastname: Johnson
authors.affiliation: Department of Medicine and Epidemiology, University of California Davis School of Veterinary Medicine, Davis, California 95616.
authors.forename: Joshua A
authors.initials: JA
authors.lastname: Stern
authors.affiliation: Department of Public Health Sciences, University of California Davis, Davis, California 95616.
authors.forename: Lihong
authors.initials: L
authors.lastname: Qi
authors.affiliation: Department of Radiation Oncology, Tokai University, Isehara, Kanagawa 259-1193, Japan.
authors.forename: Yukio
authors.initials: Y
authors.lastname: Fujita
authors.affiliation: Department of Radiology, University of California Davis School of Medicine, Sacramento, California 95817.
authors.forename: John M
authors.initials: JM
authors.lastname: Boone
mesh_terms: D000465:Algorithms; D000818:Animals; D001996:Bronchopneumonia / Q000000981:diagnostic imaging; D004285:Dogs; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D055420:Perfusion Imaging / Q000379:methods*; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D014054:Tomography / Q000379:methods*