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

Bert Vandeghinste (UGent) , Stefaan Vandenberghe (UGent) , Christian Vanhove (UGent) , Steven Staelens (UGent) and Roel Van Holen (UGent)
(2013) PLOS ONE. 8(7).
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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.
Keywords
ALLOWS, ANEURYSM, COMPUTED-TOMOGRAPHY, RECONSTRUCTION ALGORITHMS, SPECT RECONSTRUCTION, MINIMIZATION, PROJECTION DATA, EM ALGORITHM, BEAM CT, MOUSE MODEL

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MLA
Vandeghinste, Bert, et al. “Low-Dose Micro-CT Imaging for Vascular Segmentation and Analysis Using Sparse-View Acquisitions.” PLOS ONE, vol. 8, no. 7, 2013, doi:10.1371/journal.pone.0068449.
APA
Vandeghinste, B., Vandenberghe, S., Vanhove, C., Staelens, S., & Van Holen, R. (2013). Low-dose micro-CT imaging for vascular segmentation and analysis using sparse-view acquisitions. PLOS ONE, 8(7). https://doi.org/10.1371/journal.pone.0068449
Chicago author-date
Vandeghinste, Bert, Stefaan Vandenberghe, Christian Vanhove, Steven Staelens, and Roel Van Holen. 2013. “Low-Dose Micro-CT Imaging for Vascular Segmentation and Analysis Using Sparse-View Acquisitions.” PLOS ONE 8 (7). https://doi.org/10.1371/journal.pone.0068449.
Chicago author-date (all authors)
Vandeghinste, Bert, Stefaan Vandenberghe, Christian Vanhove, Steven Staelens, and Roel Van Holen. 2013. “Low-Dose Micro-CT Imaging for Vascular Segmentation and Analysis Using Sparse-View Acquisitions.” PLOS ONE 8 (7). doi:10.1371/journal.pone.0068449.
Vancouver
1.
Vandeghinste B, Vandenberghe S, Vanhove C, Staelens S, Van Holen R. Low-dose micro-CT imaging for vascular segmentation and analysis using sparse-view acquisitions. PLOS ONE. 2013;8(7).
IEEE
[1]
B. Vandeghinste, S. Vandenberghe, C. Vanhove, S. Staelens, and R. Van Holen, “Low-dose micro-CT imaging for vascular segmentation and analysis using sparse-view acquisitions,” PLOS ONE, vol. 8, no. 7, 2013.
@article{4094601,
  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.}},
  articleno    = {{e68449}},
  author       = {{Vandeghinste, Bert and Vandenberghe, Stefaan and Vanhove, Christian and Staelens, Steven and Van Holen, Roel}},
  issn         = {{1932-6203}},
  journal      = {{PLOS ONE}},
  keywords     = {{ALLOWS,ANEURYSM,COMPUTED-TOMOGRAPHY,RECONSTRUCTION ALGORITHMS,SPECT RECONSTRUCTION,MINIMIZATION,PROJECTION DATA,EM ALGORITHM,BEAM CT,MOUSE MODEL}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{10}},
  title        = {{Low-dose micro-CT imaging for vascular segmentation and analysis using sparse-view acquisitions}},
  url          = {{http://doi.org/10.1371/journal.pone.0068449}},
  volume       = {{8}},
  year         = {{2013}},
}

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