title: Proximal pulmonary vein stenosis detection in pediatric patients: value of multiplanar and 3-D VR imaging evaluation.
abstract: BACKGROUND
  One of the important benefits of using multidetector computed tomography (MDCT) is its capability to generate high-quality two-dimensional (2-D) multiplanar (MPR) and three-dimensional (3-D) images from volumetric and isotropic axial CT data. However, to the best of our knowledge, no results have been published on the potential diagnostic role of multiplanar and 3-D volume-rendered (VR) images in detecting pulmonary vein stenosis, a condition in which MDCT has recently assumed a role as the initial noninvasive imaging modality of choice.
  
  
  OBJECTIVE
  The purpose of this study was to compare diagnostic accuracy and interpretation time of axial, multiplanar and 3-D VR images for detection of proximal pulmonary vein stenosis in children, and to assess the potential added diagnostic value of multiplanar and 3-D VR images.
  
  
  METHODS
  We used our hospital information system to identify all consecutive children (< 18 years of age) with proximal pulmonary vein stenosis who had both a thoracic MDCT angiography study and a catheter-based conventional angiography within 2 months from June 2005 to February 2012. Two experienced pediatric radiologists independently reviewed each MDCT study for the presence of proximal pulmonary vein stenosis defined as ≥ 50% of luminal narrowing on axial, multiplanar and 3-D VR images. Final diagnosis was confirmed by angiographic findings. Diagnostic accuracy was compared using the z-test. Confidence level of diagnosis (scale 1-5, 5 = highest), perceived added diagnostic value (scale 1-5, 5 = highest), and interpretation time of multiplanar or 3-D VR images were compared using paired t-tests. Interobserver agreement was measured using the chance-corrected kappa coefficient.
  
  
  RESULTS
  The final study population consisted of 28 children (15 boys and 13 girls; mean age: 5.2 months). Diagnostic accuracy based on 116 individual pulmonary veins for detection of proximal pulmonary vein stenosis was 72.4% (84 of 116) for axial MDCT images, 77.5% (90 of 116 cases) for multiplanar MDCT images, and 93% (108 of 116 cases) for 3-D VR images with significantly higher accuracy with 3-D VR compared to axial (z = 4.17, P < 0.001) and multiplanar (z = 3.34, P < 0.001) images. Confidence levels for detection of proximal pulmonary vein stenosis were significantly higher with 3-D VR images (mean level: 4.6) compared to axial MDCT images (mean level: 1.7) and multiplanar MDCT images (mean level: 2.0) (paired t-tests, P < 0.001). Thus, 3-D VR images (mean added diagnostic value: 4.7) were found to provide added diagnostic value for detecting proximal pulmonary vein stenosis (paired t-test, P < 0.001); however, multiplanar MDCT images did not provide added value (paired t-test, P = 0.89). Interpretation time was significantly longer and interobserver agreement was higher when using 3-D VR images than using axial MDCT images or MPR MDCT images for diagnosing proximal pulmonary vein stenosis (paired t-tests, P < 0.001).
  
  
  CONCLUSIONS
  Use of 3-D VR images in the diagnosis of proximal pulmonary vein stenosis in children significantly increases accuracy, confidence level, added diagnostic value and interobserver agreement. Thus, the routine use of this technique should be encouraged despite its increased interpretation time.
authors.affiliation: Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA. edward.lee@childrens.harvard.edu
authors.forename: Edward Y
authors.initials: EY
authors.lastname: Lee
authors.forename: Kathy J
authors.initials: KJ
authors.lastname: Jenkins
authors.forename: Muhammad
authors.initials: M
authors.lastname: Muneeb
authors.forename: Audrey C
authors.initials: AC
authors.lastname: Marshall
authors.forename: Donald A
authors.initials: DA
authors.lastname: Tracy
authors.forename: David
authors.initials: D
authors.lastname: Zurakowski
authors.forename: Phillip M
authors.initials: PM
authors.lastname: Boiselle
mesh_terms: D000465:Algorithms; D002675:Child, Preschool; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007223:Infant; D008297:Male; D010690:Phlebography / Q000379:methods*; D011668:Pulmonary Veno-Occlusive Disease / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*