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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorWang, Ge, 1957-
dc.contributorIntes, Xavier
dc.contributorYazici, Birsen
dc.contributorLai, W. Michael, 1930-
dc.contributor.authorYang, Qingsong
dc.date.accessioned2021-11-03T09:05:10Z
dc.date.available2021-11-03T09:05:10Z
dc.date.created2018-10-24T13:39:11Z
dc.date.issued2018-08
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2276
dc.descriptionAugust 2018
dc.descriptionSchool of Engineering
dc.description.abstractX-ray computed tomography (CT) has become one of key biomedical imaging tools since its invention. This thesis addresses several frontier problems in the field of x-ray CT systems. X-ray phase contrast imaging (PCI) is a promising development in the field of biomedical imaging and promises to enhance or enable important imaging applications. PCI provides not only attenuation-based images (the same as those produced in the traditional CT/micro-CT), but also highly complementary phase contrast and dark field information (allowing more insights into the structures of biomedical tissues). In the first study, a standalone PCI system as a Talbot interferometer was built and optimized. On this platform, preliminary data were obtained to validate and evaluate the imaging performance. Following the system setup, in the second study, a ring artifact removal method was proposed to improve the image quality of the built system.
dc.description.abstractExperimental 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.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectBiomedical engineering
dc.titleX-ray grating-based multi-contrast imaging system and methods
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid179277
dc.digitool.pid179278
dc.digitool.pid179279
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Biomedical Engineering


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