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Low-dose micro-CT imaging for vascular segmentation and analysis using sparse-view acquisitions

Journal Contribution - e-publication

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.
Journal: PLoS ONE
ISSN: 1932-6203
Volume: 8
Pages: 1 - 10
Publication year:2013
Keywords:A1 Journal article
BOF-keylabel:yes
BOF-publication weight:3
CSS-citation score:1
Authors from:Higher Education
Accessibility:Open