Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

© 2016 IOP Publishing Ltd. Micro-computed tomography systems are widely used for high-resolution, non-destructive analysis of internal microvascular networks. When the scale of the targeted vessel approaches the imaging resolution limit, the level of noise becomes a limiting factor for accurate reconstruction. Denoising algorithms provided byvendors are often suboptimal for enhancing SNR of fine (vessel) features. Furthermore, the performance ofexisting methods has not been systematically analyzedinthe context offinal network reconstruction and graph model extraction. This work evaluates several standard and state-of-the-art noise reduction techniques using both in silico and physical phantoms, and ex vivo rat coronary data for their abilitytoimprove vascular network analysis.Wecomparedfive noise reduction approaches, including vendor-supplied (Gaussian smoothing), conventional (median filter) and advanced (i.e. wavelet filter with soft thresholding, block-matching collaborative filtering (BM3D), and isotropic and anisotropic total variation denoising) techniques. The latter two methods were chosen for their reported ability to preserve fine details, a prerequisite for a successful microvascular extraction. The full evaluation pipeline included the reconstruction from projection images, denoising, vascular segmentation and graph model extractionto beperformed onall simulated and real image data sets. SNR, CNR and 3DNPS were quantified from denoised images, and where the ground truth was known, Sørensen-Dice coefficients, Jaccard index metrics were calculated as measures of segmentation error. The performance ofthe image denoising algorithms where the ground-truth was available has been assessed bycomputing the correlation coefficients between the residual images (obtained between the noise-free data and the denoised data) and the first derivative of the noise-free data were computed. Overall, simpler denoising techniques including the median and waveletfilters and the vendor-supplied implementations have been foundtoperform inadequately for segmentation of fine vessel features, particularlyon real images. BM3D technique performed well in most of our tests, however isotropic total variation (ITV) was the optimal choice for noise reduction and feature preservation in real data as shownbythe extracted network models. Globally, ITV increased the SNR from 10.2 to31.7 dB in aShepp Logan phantom, doubled SNR and CNR valuesina scanned physical phantom compared with BM3D, enabled the smallest vesselsto befully recovered in aninsilicon phantom and achieved anear-ideal outcomeinthe rat coronary data.

Original publication




Journal article


Biomedical Physics and Engineering Express

Publication Date