title: Automatic detection of aorto-femoral vessel trajectory from whole-body computed tomography angiography data sets.
abstract: Extraction of the aorto-femoral vessel trajectory is important to utilize computed tomography angiography (CTA) in an integrated workflow of the image-guided work-up prior to trans-catheter aortic valve replacement (TAVR). The aim of this study was to develop a new, fully-automated technique for the extraction of the entire arterial access route from the femoral artery to the aortic root. An automatic vessel tracking algorithm was first used to find the centerline that connected the femoral accessing points and the aortic root. Subsequently, a deformable 3D-model fitting method was used to delineate the lumen boundary of the vascular trajectory in the whole-body CTA dataset. A validation was carried out by comparing the automatically obtained results with semi-automatically obtained results from two experienced observers. The whole framework was validated on whole body CTA datasets of 36 patients. The average Dice similarity indexes between the segmentations of the automatic method and observer 1 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta were 0.977 ± 0.030, 0.980 ± 0.019, 0.982 ± 0.016; the average Dice similarity indexes between the segmentations of the automatic method and observer 2 were 0.950 ± 0.040, 0.954 ± 0.031 and 0.965 ± 0.019, respectively. The inter-observer variability resulted in a Dice similarity index of 0.954 ± 0.038, 0.952 ± 0.031 and 0.969 ± 0.018 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta. The average minimal luminal diameters (MLDs) of the ilio-femoral artery were 6.03 ± 1.48, 5.70 ± 1.43 and 5.52 ± 1.32 mm for the automatic method, observer 1 and observer 2 respectively. The MLDs of the aorta were 13.43 ± 2.54, 12.40 ± 2.93 and 12.08 ± 2.40 mm for the automatic method, observer 1 and observer 2 respectively. The automatic measurement overestimated the MLD slightly in the ilio-femoral artery at the average by 0.323 mm (SD = 0.49 mm, p < 0.001) compared to observer 1 and by 0.51 mm (SD = 0.71 mm, p < 0.001) compared to observer 2. The proposed segmentation approach can automatically provide reliable measurements of the entire arterial accessing route that can be used to support TAVR procedures. To the best of our knowledges, this approach is the first fully automatic segmentation method of the whole aorto-femoral vessel trajectory in CTA images.
authors.affiliation: Division of Image Processing, Department of Radiology, Leiden University Medical Center, 9600, 2300 RC, Leiden, The Netherlands.
authors.forename: Xinpei
authors.initials: X
authors.lastname: Gao
authors.affiliation: Division of Image Processing, Department of Radiology, Leiden University Medical Center, 9600, 2300 RC, Leiden, The Netherlands. P.H.Kitslaar@lumc.nl.|Medis medical imaging systems b.v., Leiden, The Netherlands. P.H.Kitslaar@lumc.nl.
authors.forename: Pieter H
authors.initials: PH
authors.lastname: Kitslaar
authors.affiliation: Department of Radiology, University Medical Center Rotterdam, Rotterdam, The Netherlands.
authors.forename: Ricardo P J
authors.initials: RP
authors.lastname: Budde
authors.affiliation: Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Shengxian
authors.initials: S
authors.lastname: Tu
authors.affiliation: Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Michiel A
authors.initials: MA
authors.lastname: de Graaf
authors.affiliation: Catheterization Laboratory, National Center for Cardiovascular Diseases of China and Fu Wai Hospital, Beijing, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Xu
authors.affiliation: Catheterization Laboratory, National Center for Cardiovascular Diseases of China and Fu Wai Hospital, Beijing, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Xu
authors.affiliation: Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Arthur J H A
authors.initials: AJ
authors.lastname: Scholte
authors.affiliation: Division of Image Processing, Department of Radiology, Leiden University Medical Center, 9600, 2300 RC, Leiden, The Netherlands.
authors.forename: Jouke
authors.initials: J
authors.lastname: Dijkstra
authors.affiliation: Division of Image Processing, Department of Radiology, Leiden University Medical Center, 9600, 2300 RC, Leiden, The Netherlands.|Medis medical imaging systems b.v., Leiden, The Netherlands.
authors.forename: Johan H C
authors.initials: JH
authors.lastname: Reiber
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001011:Aorta / Q000000981:diagnostic imaging*; D001021:Aortic Valve / Q000000981:diagnostic imaging; D001331:Automation; D006328:Cardiac Catheterization / Q000379:methods; D000072226:Computed Tomography Angiography / Q000379:methods*; D005260:Female; D005263:Femoral Artery / Q000000981:diagnostic imaging*; D019918:Heart Valve Prosthesis Implantation / Q000379:methods; D006801:Humans; D007083:Iliac Artery / Q000000981:diagnostic imaging*; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D015588:Observer Variation; D066230:Patient-Specific Modeling; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D051598:Whole Body Imaging / Q000379:methods*; D057188:Workflow
keywords: Aorto-femoral; CTA; Fully-automatic; Segmentation; TAVR