title: Low-dose micro-CT imaging for vascular segmentation and analysis using sparse-view acquisitions.
abstract: The aim of this study is to investigate whether reliable and accurate 3D geometrical models of the murine aortic arch can be constructed from sparse-view data in vivo micro-CT acquisitions. This would considerably reduce acquisition time and X-ray dose. In vivo contrast-enhanced micro-CT datasets were reconstructed using a conventional filtered back projection algorithm (FDK), the image space reconstruction algorithm (ISRA) and total variation regularized ISRA (ISRA-TV). The reconstructed images were then semi-automatically segmented. Segmentations of high- and low-dose protocols were compared and evaluated based on voxel classification, 3D model diameters and centerline differences. FDK reconstruction does not lead to accurate segmentation in the case of low-view acquisitions. ISRA manages accurate segmentation with 1024 or more projection views. ISRA-TV needs a minimum of 256 views. These results indicate that accurate vascular models can be obtained from micro-CT scans with 8 times less X-ray dose and acquisition time, as long as regularized iterative reconstruction is used.
authors.affiliation: Institute Biomedical Technology, MEDISIP, Ghent University-iMinds, Ghent, Belgium. Bert.Vandeghinste@UGent.be
authors.forename: Bert
authors.initials: B
authors.lastname: Vandeghinste
authors.forename: Stefaan
authors.initials: S
authors.lastname: Vandenberghe
authors.forename: Chris
authors.initials: C
authors.lastname: Vanhove
authors.forename: Steven
authors.initials: S
authors.lastname: Staelens
authors.forename: Roel
authors.initials: R
authors.lastname: Van Holen
mesh_terms: D000465:Algorithms; D000818:Animals; D001013:Aorta, Thoracic / Q000000981:diagnostic imaging*; D006321:Heart / Q000000981:diagnostic imaging*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D011829:Radiation Dosage; D055114:X-Ray Microtomography / Q000191:economics / Q000379:methods*; D014965:X-Rays