title: Clinical validation of a 4D-CT based method for lung ventilation measurement in phantoms and patients.
abstract: BACKGROUND
  Lung cancer patients referred to radiotherapy (RT) often present with regional lung function deficits, and it is therefore of interest to image their lung function prior to treatment. In this study a method was developed that uses a deformable image registration (DIR) between the peak-inhale and peak-exhale phases of a thoracic four-dimensional computed tomography (4D-CT) scan to extract ventilation information. The method calculates the displacement vector fields (DVFs) resulting from the DIR using the Jacobian map approach in order to extract information regarding regional lung volume change.
  
  
  METHODS
  The DVFs resulting from DIRs were analysed to compute the Jacobian determinant of vectors in the field, thus obtaining a map of the vector gradients of the entire registered CT image, i.e. voxel-wise local volume change. Geometric and quantitative validation was achieved using images of both phantoms and patients. In the phantom studies, translations and deformations of known size and direction were introduced to validate both the DIR algorithm and the method as a whole. Furthermore, five patients underwent 4D-CT for planning of stereotactic body RT (SBRT). The patients were immobilised in a stereotactic body frame (SBF) and for each patient, two thoracic 4D-CT scans were acquired, one scan with respiration restricted by an abdominal compression plate and the other under free breathing.
  
  
  RESULTS
  In the phantom studies deformation errors were found to be of the order of the expected precision of 3 mm, corresponding to the image slice distance, in lateral and vertical directions. For the longitudinal direction a more pronounced discrepancy was observed, with the algorithm predicting displacement lengths of less than half of the physically introduced deformation. Qualitatively the method performed as expected. In the patient study an inverse consistency test showed deviations of up to 5.8 mm, i.e. almost twice the image slice separation. Jacobian maps of the patient images indicated well-ventilated areas as anatomically expected.
  
  
  CONCLUSIONS
  The established method provides a means of using a (commercially available) DIR algorithm to obtain a quantitative measure of local lung volume change. With further phantom and patient validation studies, quantitative maps of specific ventilation should be possible to produce and use in a clinical setting.
authors.affiliation: Departments of Medical Physics and Oncology, Aarhus University Hospital/Aarhus University, Aarhus, Denmark.
authors.forename: Tine B
authors.initials: TB
authors.lastname: Nyeng
authors.forename: Jesper F
authors.initials: JF
authors.lastname: Kallehauge
authors.forename: Morten
authors.initials: M
authors.lastname: Høyer
authors.forename: Jørgen B B
authors.initials: JB
authors.lastname: Petersen
authors.forename: Per R
authors.initials: PR
authors.lastname: Poulsen
authors.forename: Ludvig P
authors.initials: LP
authors.lastname: Muren
mesh_terms: D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy; D056973:Four-Dimensional Computed Tomography; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy; D019047:Phantoms, Imaging; D011379:Prognosis; D012123:Pulmonary Ventilation; D011857:Radiographic Image Interpretation, Computer-Assisted; D011877:Radionuclide Imaging; D016634:Radiosurgery; D050397:Radiotherapy, Intensity-Modulated; D012119:Respiration; D014057:Tomography, X-Ray Computed