Lower extremity CT angiography (CTA): initial evaluation of a knowledge-based centerline estimation algorithm for femoro-popliteal artery (FPA) occlusions.
OBJECTIVE
Existing density- and gradient-based automated centerline-extraction algorithms fail in severely diseased or occluded arterial segments for the generation of curved planar reformations (CPRs). We aimed to quantitatively and qualitatively assess the precision of a knowledge-based centerline-extraction algorithm in patients with occluded femoro-popliteal artery (FPA).


METHODS
Computed tomography angiograms of 38 FPA occlusions (mean length 120 mm) were retrospectively identified. Reference centerlines were determined as the mean of eight manual expert readings. Each occlusion was also interpolated using a new knowledge-based algorithm (partial vector space projection [PVSP]), which uses shape information extracted from a separate database of 30 nondiseased FPAs. Precision of PVSP was quantified as the maximum departure error (MDE) from the standard of reference and the proportion of the interpolated centerlines remaining within an assumed vessel radius of 3 mm. Multiple regression method was used to determine the factors predicting the precision of the algorithm. CPR quality was independently assigned by two readers.


RESULTS
The mean MDE (in mm) for occlusion lengths of <50 mm, 50-100 mm, 100-200 mm, and >200 mm was 0.95, 1.19, 1.40, and 2.25, for manual readings and 1.68, 2.90, 9.43, and 19.95 for PVSP, respectively. MDEs of the algorithm were completely contained within 3 mm of the assumed vessel radius in 20 of 38 occlusions. CPR quality was rated diagnostic by both readers in 23 of 38 occlusions.


CONCLUSIONS
Shape-based centerline extraction of FPA occlusions in lower extremity CTA is feasible, and independent from local density and gradient information. PVSP centerline extraction allows interpolation of occlusions up to 100 mm within the variability of manually derived centerlines.
Roos
Justus E
JE
Department of Radiology, Stanford University Medical Center, 300 Pasteur Drive, Room S-072, Stanford, CA 94305-5105, USA. justus.roos@stanford.edu
Rakshe
Tejas
T
Tran
David N
DN
Rosenberg
Jarrett
J
Straka
Matus
M
El-Helw
Tamer
T
Sofilos
Marc C
MC
Napel
Sandy
S
Fleischmann
Dominik
D
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001157:Arterial Occlusive Diseases / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D003198:Computer Simulation; D005260:Female; D005263:Femoral Artery / Q000000981:diagnostic imaging; D006801:Humans; D035002:Lower Extremity / Q000098:blood supply* / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods; D016491:Peripheral Vascular Diseases / Q000000981:diagnostic imaging*; D011150:Popliteal Artery / 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*