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Tomography of Materials and Structures is a peer-reviewed journal published by Elsevier, covering Advanced X-ray and CT Imaging, Medical Imaging Techniques and Applications, Advanced X-ray Imaging Techniques.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Tomography of Materials and Structures 12 (2023) 45–58.
Formats any DOI in Tomography of Materials and Structures style. No sign-up. |
| Publishes research in | Advanced X-ray and CT Imaging Medical Imaging Techniques and Applications Advanced X-ray Imaging Techniques Welding Techniques and Residual Stresses Radiation Dose and Imaging |
| ISSN | 2949-673X |
| Citation impact (2-yr) | 2.1 |
| h-index | 8 |
| i10-index | 7 |
| Total citations | 248 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Within the past decade, X-ray micro computed tomography (µCT) has become an advanced non-destructive tool to analyse the internal structure of opaque materials. In addition to high spatial resolution, new generations of laboratory-based µCT machines and synchrotron imaging facilities can achieve high temporal resolution. This makes µCT the method of choice to study dynamics processes…
We investigate appliance of different deep learning models to the problem of semantic segmentation of structural defects in computed tomography images of fiber-reinforced polymer composite material. Specifically, we try to segment porosities and delaminations in a spiecement using U-Net and DeepLabv3 neural networks. We find out that complex models struggle to generalize solutions on small…
Image segmentation with deep learning models has significantly improved the accuracy of the pixel-wise labeling of scientific imaging which is critical for many quantitative image analyses. This has been feasible through U-Net and related architecture convolutional neural network models. Although the adoption of these models has been widespread, their training data pool and hyperparameters have…
Limited-angle computed tomography is often imposed by in-situ experiments combining tomography with sample environments. The missing projection data causes artifacts in the tomographic reconstruction. We demonstrate that the correction of these numerical artifacts can be achieved by restoring the missing projections using an iterative reconstruction scheme. The reconstruction is regularized using segmentation, and thresholds determined…
