title: Airway tree reconstruction in expiration chest CT scans facilitated by information transfer from corresponding inspiration scans.
abstract: OBJECTIVE
  Analysis and comparison of airways imaged in pairs of inspiration and expiration lung CT scans provides important information for quantitative assessment of lung diseases like chronic obstructive pulmonary disease. However, airway tree reconstruction in expiration CT scans is a challenging problem. Typically, only a low number of airway branches are found in expiration scans, compared to inspiration scans. To detect more airways in expiration CT scans, the authors introduce a novel airway reconstruction approach and assess its performance.
  
  
  METHODS
  The method requires a pair of inspiration and expiration CT scans and utilizes information from the inspiration scan to facilitate reconstructing the airway tree in the expiration lung CT scan. First, an initial airway tree (high confidence) and airway candidates (limited confidence) are reconstructed in the expiration scan by utilizing a 3D graph-based reconstruction method. Then, the 3D airway tree is reconstructed in the inspiration scan. Second, correspondences between expiration and inspiration tree structures are identified by utilizing a novel hierarchical tree matching algorithm that utilizes a local CT image-based similarity criterion. Third, the tree information from the inspiration airway tree is used to select expiration candidates, resulting in the final expiration tree structure. The approach was evaluated on a diverse set of 40 scan pairs and compared to the baseline method, which utilizes only the expiration CT scan.
  
  
  RESULTS
  The proposed method produced a significant (p < 0.05) increase in airway tree length by 13.35 cm, on average, which represents an 11.21% increase relative to the baseline result using only the expiration CT scan. A detailed analysis of all additionally identified airways resulted in a true and false positive rate of 94.8% and 5.2%, respectively. The true positive rate was found to be significantly higher than the false positive rate (p < 0.05).
  
  
  CONCLUSIONS
  The proposed method allowed increasing the number of found airways in expiration scans significantly. In addition, the algorithm establishes correspondence between inspiration and expiration airway trees, which can facilitate label transfer between airway trees and quantitative assessment of change in airways. The approach can be adapted to facilitate airway reconstruction in several longitudinal lung CT scans by means of mutual information transfer.
authors.affiliation: Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242 and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242.
authors.forename: Christian
authors.initials: C
authors.lastname: Bauer
authors.affiliation: Department of Internal Medicine, The University of Iowa Carver College of Medicine, Iowa City, Iowa 52242.
authors.forename: Michael
authors.initials: M
authors.lastname: Eberlein
authors.affiliation: Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242; The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242; and Department of Internal Medicine, The University of Iowa Carver College of Medicine, Iowa City, Iowa 52242.
authors.forename: Reinhard R
authors.initials: RR
authors.lastname: Beichel
mesh_terms: D045853:Exhalation; D006801:Humans; D021621:Imaging, Three-Dimensional; D001239:Inhalation; D008168:Lung / Q000033:anatomy & histology* / Q000000981:diagnostic imaging* / Q000502:physiology; D013902:Radiography, Thoracic; D014057:Tomography, X-Ray Computed