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.
Highlights
Mouse models provide valuable information about the development and progression of cardiovascular pathologies within a reasonable timeframe
A small loss of spatial resolution can be noted in all image space reconstruction algorithm (ISRA)-Total Variation (TV) reconstructions, compared to ISRA without regularization
Based on the results presented, we found that 256-view acquisitions can result in comparable segmentations as full-view acquisitions, as long as TV regularization is used
Summary
Mouse models provide valuable information about the development and progression of cardiovascular pathologies within a reasonable timeframe. In order to perform such research, reliable 3D models of the cardiovascular system have to be made. These models are used as input for Computation Fluid Dynamic (CFD) simulations. Ex vivo techniques using vascular corrosion casting [8] proved to be a good tool, resulting in high quality models [9]. These ex vivo techniques eliminate the possibility to gather longitudinal information, which is considered crucial in evaluating pathology evolution and in therapy development [10]
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