Objective: To evaluate automatic vessel tracking techniques in the course of preoperative planning prior to transluminal aortic endograft implantation by comparing accuracy, reproducibility, and postprocessing time with source image and volume-rendered analysis methods.
Methods: Multislice computed tomography datasets of 5 patients with abdominal aortic aneurysms were preoperatively examined, performing volumetric analysis of diameter and position of renal artery orifices, aneurysmal neck, maximal aneurysmal extension, aortic bifurcation, and iliac arteries and bifurcation. Analysis was realized by utilizing transverse datasets, volume rendering, and automated vessel tracking strategies (MxView, Philips, Best, The Netherlands). Measurement techniques were evaluated by 2 independent readers 3 times for each patient and measurement modality. Statistical analysis evaluated accuracy of the measurements and intra- and interobserver reliability. Postprocessing time was documented.
Results: Using transverse source datasets, intraobserver reliability ranged from 0.49 to 0.58. Intraobserver reliability improved to 0.7 to 0.98 when volume-rendered datasets were evaluated. Interobserver variability for transverse and volume-rendered datasets ranged from 0.49 to 0.76 and 0.70 to 0.96, respectively. Automated vessel tracking datasets did not demonstrate any intra- or interobserver variability. Based on transverse datasets, the length and diameter of iliac arteries and location and diameter of the aneurysmal neck were measured as statistically different in all cases in contrast to volume rendering and automated segmentation techniques. Postprocessing time consumption for measurements based on transverse, volume-rendered, and automated tracking segmentation datasets averaged 3.32 minutes, 25.43 minutes, and 2.24 minutes, respectively.
Conclusions: Preoperative measurements improve significantly if datasets are evaluated based on volume-rendering techniques. This time-consuming procedure can be shortened, while further reducing observer variability, with automatic segmentation techniques.