Document Detail

Lower extremity CT angiography (CTA): initial evaluation of a knowledge-based centerline estimation algorithm for femoro-popliteal artery (FPA) occlusions.
MedLine Citation:
PMID:  19427978     Owner:  NLM     Status:  MEDLINE    
RATIONALE AND OBJECTIVES: 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). MATERIAL AND 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. CONCLUSION: 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.
Justus E Roos; Tejas Rakshe; David N Tran; Jarrett Rosenberg; Matus Straka; Tamer El-Helw; Marc C Sofilos; Sandy Napel; Dominik Fleischmann
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Academic radiology     Volume:  16     ISSN:  1878-4046     ISO Abbreviation:  Acad Radiol     Publication Date:  2009 Jun 
Date Detail:
Created Date:  2009-05-11     Completed Date:  2009-07-22     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9440159     Medline TA:  Acad Radiol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  646-53     Citation Subset:  IM    
Department of Radiology, Stanford University Medical Center, 300 Pasteur Drive, Room S-072, Stanford, CA 94305-5105, USA.
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MeSH Terms
Aged, 80 and over
Angiography / methods*
Arterial Occlusive Diseases / radiography*
Artificial Intelligence*
Computer Simulation
Femoral Artery / radiography
Lower Extremity / blood supply*,  radiography*
Middle Aged
Models, Cardiovascular
Pattern Recognition, Automated / methods
Peripheral Vascular Diseases / radiography*
Popliteal Artery / radiography
Radiographic Image Enhancement / methods
Radiographic Image Interpretation, Computer-Assisted / methods
Reproducibility of Results
Sensitivity and Specificity
Tomography, X-Ray Computed / methods*
Grant Support
Comment In:
Acad Radiol. 2009 Jun;16(6):643-5   [PMID:  19427977 ]

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