Abstract
This work presents a framework to exploit the synergy between Digital Volume Correlation (DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-µCT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360° rotation.
Highlights
High-resolution X-ray Computed Tomography or μCT is a valuable non-destructive 3D imaging technique in numerous research areas
While Digital Volume Correlation (DVC) has found its way to μCT material research, its potential to compensate for motion blurring has not been given much attention, as in most applications both the reference and deformed images are derived from high quality static CT scans
When the dynamics of a deformation process are at the edge of a tractable temporal resolution for imaging, and are not controllable, i.e. there is no clear distinction between reference and deformed states, DVC and CT reconstruction can be integrated to enhance the quality of both, in terms of signal-to-noise ratio (SNR), temporal and spatial resolution
Summary
High-resolution X-ray Computed Tomography or μCT is a valuable non-destructive 3D imaging technique in numerous research areas. Though compared with synchrotron sources they are advantageous in terms of system cost and offer the freedom to move the X-ray source, the major limitation for the lab-based setups is the limited X-ray flux produced by micro-focused X-ray tubes This subsequently limits the temporal resolution due to a lack of photon statistics at the lowest detector exposure times. We consider a dynamic non-rigid deformation of the sample’s micro-structure An estimate of this local deformation could serve as a priori knowledge to compensate for motion blurring upon its integration in a CT reconstruction algorithm. While DVC has found its way to μCT material research, its potential to compensate for motion blurring has not been given much attention, as in most applications both the reference and deformed images are derived from high quality static CT scans. We show that the framework is capable of successfully compensating for motion artefacts, even in a conventional μCT scan using only a single rotation where sample movement is unintended
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