Porous structures are widely found in natural and engineered material systems. To study the defect initialization and damage evolution in the complex 3D network structures, we explore advanced X-ray phase tomography to provide holistic and high-resolution 3D data. A pipeline of deep learning-based phase retrieval, computer vision, and damage identification algorithms are implemented to extract various types of damage for large volumetric tomography data. We first obtain high-quality phase tomography reconstruction from noisy and insufficient CT acquisition. Based on a hybrid approach using both model-based feature filtering and data-driven machine learning, we then identifies the defects and damaged regions from the background of porous structures. This method is applied to an in-situ X-ray tomography measurement on a natural cellular material; the accurate and comprehensive defects detection reveals insight into 3D damage evolution modes for porous material systems.
X-ray computed tomography has been recently applied to capture the dynamic behaviors of complex material systems in 4D. The dynamic 3D acquisition, however, usually leads to insufficient data acquisition with low-dose X-ray radiation and limited-angle projections. A high-fidelity CT reconstruction is challenging based on the severely limited acquisition. While prior constraint, such as local smoothness, can improve the quality of reconstructions, a more general reconstruction strategy to include structural features on a range of different scales proves to yield better reconstruction results and are more adaptive to complex structured materials. In this work, we develop the hierarchical synthesis network to establish structural priors for sparse-view CT reconstruction, which achieves high-fidelity with an improved computation efficiency. We found that the established knowledge of structural priors on each different scale can be independently transferred to sparse-view CT reconstruction under different conditions, enabling the transfer of non-local features into the reconstruction of a phase tomography application.
We present a hierarchical imaging reconstruction algorithm for a 3D phase tomography based on the densely extracted features on a multi-band pyramid of convolutional network. By implementing a layer-wise hierarchical machine learning network and combine different bands of information for the imaging retrieval, a more efficient and adaptive learning strategy is established to enable an accurate reconstruction with fewer training data and improved accuracy. In addition, the distinction of intensity and spectral bands in the feature training process enables bias correction for reconstruction under varied conditions. In particular, we demonstrate a robust imaging reconstruction for a sparse-view phase tomography application, where spectrally biased phase diffraction and intensity-biased photon noise are both successfully corrected for.
We present a high-temporal resolution 4D-XCT with feature-based iterative reconstruction method(FBIR) by imposing feature priors in the reconstruction process. The 4D reconstruction is acquired through an iterative minimization of the cost function which is obtained by combining the forward model and multiple structural featurebased priors. The scheme is applied to the study of the mechanical response of a porous structure (sea urchin spines), which achieves high temporal resolution and demonstrates robustness against noise, limited views and motion induced blurring.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.