title: Multidetector CT angiography of arterial inflow and runoff in the lower extremities: a challenge in data acquisition and evaluation.
abstract: OBJECTIVE
  To show the feasibility of acquiring homogenous 3-dimensional datasets with high temporal and spatial resolution from computed tomographic angiographic (CTA) scans of the lower extremities and to assess automated vessel-tracking techniques for vascular evaluation.
  
  
  METHODS
  Eighteen men (mean age 67.0 years, range 43-83) with aneurysmal or occlusive vascular diseases underwent contrast-enhanced CTA of the lower limb arteries utilizing a 16-row CT imager. Curved multiplanar reformations were generated by manual selection of vessel centerlines in the infrarenal aorta and the arterial vasculature in the pelvis, thigh, and calf based on volume-rendering techniques. For each vessel, opacification and depiction were quantitatively evaluated. The manually segmented images were compared to datasets processed with automated vessel-tracking strategies by 5 radiologists, who evaluated diagnostic reliability and image quality. A Differential Receiver Operating Characteristic (DROC) analysis was performed.
  
  
  RESULTS
  An increase in the temporal and spatial resolution led to acquisition of high quality CTA datasets. Significant homogeneity of the vascular contrast-to-noise ratios was achieved in the pelvic (coefficient of variance 1.5% to 10.1%), thigh (0.1% to 9.4%), and calf (3.3% to 19.2%) vessels. The assessment of vascular delineation revealed full-width-at-half-maximum contrast values of 96.4%, 95.5%, and 111.3% in the pelvis, thigh, and calf, respectively. Observers were not able to distinguish between manual and automated vascular segmentation, as represented by a 0.56 value for the area under the DROC curve.
  
  
  CONCLUSIONS
  High-resolution CTA lower extremity datasets were acquired successfully, presenting vascular signal intensities of high homogeneity suitable for automated vessel-tracking techniques. Automated 3D visualization tools produced reliable, reproducible, and time-efficient centerline extractions that were comparable to manually defined centerlines.
authors.affiliation: Department of Radiology, Case Western Reserve University, University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, OH 4410-106, USA.
authors.forename: Daniel T
authors.initials: DT
authors.lastname: Boll
authors.forename: Jonathan S
authors.initials: JS
authors.lastname: Lewin
authors.forename: Thorsten R
authors.initials: TR
authors.lastname: Fleiter
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Duerk
authors.forename: Elmar M
authors.initials: EM
authors.lastname: Merkle
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000783:Aneurysm / Q000000981:diagnostic imaging*; D001157:Arterial Occlusive Diseases / Q000000981:diagnostic imaging*; D006801:Humans; D007083:Iliac Artery / Q000000981:diagnostic imaging; D007091:Image Processing, Computer-Assisted; D007866:Leg / Q000098:blood supply*; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D011859:Radiography; D012189:Retrospective Studies; D016909:Tibial Arteries / Q000000981:diagnostic imaging