X-ray grating-based multi-contrast imaging system and methods

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Authors
Yang, Qingsong
Issue Date
2018-08
Type
Electronic thesis
Thesis
Language
ENG
Keywords
Biomedical engineering
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Abstract
Experimental data have demonstrated that the proposed method effectively suppresses the ring artifacts in the reconstructed phase images. The third study dealt with the data truncation problem in differential phase data reconstruction. An interior tomographic algorithm was developed for the grating system when projection data were truncated due to limited dimensions of the gratings. Furthermore, spectral CT with photon-counting detectors is another important development. The fourth and fifth studies focus on novel multi-energy image reconstruction algorithms. A reconstruction algorithm based on low-rank constraint was developed and optimized for hybrid spectral-CT image reconstruction. The multi-contrast image reconstruction algorithm can also be adapted to the triple-contrast image reconstruction for the grating imaging system. The final study was focused on CT image denoising with the deep learning technology. An end-to-end neural network was trained according to the perceptual and adversarial losses. With a noisy CT image as input, the network produced a much-improved version. Experiments with medical CT images demonstrated that the network had a decent capability of denoising while keeping most image details. In summary, this thesis includes both hardware setup and algorithm development; solving problems related to system setup, data acquisition, image reconstruction and post-processing. All the results in this thesis could be integrated into a multi-contrast imaging chain for pre-clinical and clinical study.
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August 2018
School of Engineering
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Rensselaer Polytechnic Institute, Troy, NY
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