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Low-Dose Micro-CT Imaging for Vascular Segmentation and Analysis Using Sparse-View Acquisitions

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  • Bert Vandeghinste
  • Stefaan Vandenberghe
  • Chris Vanhove
  • Steven Staelens
  • Roel Van Holen

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.

Suggested Citation

  • Bert Vandeghinste & Stefaan Vandenberghe & Chris Vanhove & Steven Staelens & Roel Van Holen, 2013. "Low-Dose Micro-CT Imaging for Vascular Segmentation and Analysis Using Sparse-View Acquisitions," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-10, July.
    • Handle: RePEc:plo:pone00:0068449
    DOI: 10.1371/journal.pone.0068449
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    References listed on IDEAS

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    1. Bram Trachet & Abigail Swillens & Denis Van Loo & Christophe Casteleyn & Anne De Paepe & Bart Loeys & Patrick Segers, 2009. "The influence of aortic dimensions on calculated wall shear stress in the mouse aortic arch," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 12(5), pages 491-499.
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    Cited by:

    1. Wei Yu & Li Zeng, 2014. "A Novel Weighted Total Difference Based Image Reconstruction Algorithm for Few-View Computed Tomography," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.

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