Journal of X-Ray Science and Technology - Volume 24, issue 1

Authors: Chen-Chen, Xia | Yadav, Arun Kumar | Kai, Zhang | Yi-Feng, Peng | Qing-Xi, Yuan | Pei-Ping, Zhu | Li-Jin, Feng | Xu-Dong, Xu | A-Shan, Wu | Guang-Yu, Tang

Article Type: Research Article

Abstract: Objective: The aim of this study is to investigate microstructural changes in chronic glomerulonephritis (CGN) rabbit model under diffraction enhanced imaging (DEI) technology of synchrotron radiation (SR). Materials and Methods: The chronic glomerulonephritis (CGN) models were obtained within two months after 5 New Zealand white rabbits were treated with doxorubicin hydrochloride. Blood exams, urine tests and kidney histological studies were carried out after the 5 rabbits were humanely sacrificed by hyperanesthesia. The kidney tissues were fixed in 4% formalin for one week before DEI experiment, with another 5 normal rabbits used as the control group. The experiment was …performed at Beijing Synchrotron Radiation Facility (BSRF) with a 4W1A beam line (beam energy was 14keV). On routine scanning process, the rocking curve was detected, and slope position on the curve was selected to make a 360° spatial CT scan; DEI reconstruction software was used to generate a 3-dimensional image, from which the difference in grey value between the chronic glomerulonephritis (CGN) group and the control group was measured and analyzed using MATLAB and SPSS. Result: Without radio-contrast, DEI provided clear visibility of the microstructures including artery, vein, straight collecting ducts, papillary tubules, glomeruli in both the chronic glomerulonephritis (CGN) group and the control group, with a spatial resolution as low as 10μ m. MATLAB grey value extraction and SPSS analysis showed that cortex of CGN group (91 to 112) lost more gray value compared to the control group (121 to 141), T tests P  <  0.05. Equivalant cortical ROI (data points 450×80) quantitative analysis showed that gross grey value of CGN group (ranking from 55 to 160) was smaller than the control group (ranking from 75 to 175). DEI images correlated well with pathologic images. Morphological changes in the microstructure of contstartabstractCGN kidney was revealed, due to the advantage of phase-contrast imaging (PCI) mechanism, and the diagnostic value of CGN by synchrotron radiation (SR) phase-contrast imaging (PCI) technology was evaluated. Conclusion: Synchrotron radiation (SR) diffraction enhanced imaging (DEI) experiment makes non-contrast CGN diagnosis possible in the rabbit model studied. With improvement of laboratory equipment and image analyzer in clinical practice, diffraction enhanced imaging (DEI) could fundamentally become a new diagnostic method for CGN. Show more

Keywords: Synchrotron radiation (SR), phase contrast imaging (PCI), diffraction enhanced imaging (DEI), chronic glomerulonephritis (CGN)

DOI: 10.3233/XST-160534

Citation: Journal of X-Ray Science and Technology, vol. 24, no. 1, pp. 145-159, 2016

Authors: Li, Liang | Wang, Bigong | Wang, Ge

Article Type: Research Article

Abstract: In this paper, we formulate the joint/simultaneous X-ray CT and MRI image reconstruction. In particular, a novel algorithm is proposed for MRI image reconstruction from highly under-sampled MRI data and CT images. It consists of two steps. First, a training dataset is generated from a series of well-registered MRI and CT images on the same patients. Then, an initial MRI image of a patient can be reconstructed via edge-oriented dual-dictionary guided enrichment (EDGE) based on the training dataset and a CT image of the patient. Second, an MRI image is reconstructed using the dictionary learning (DL) algorithm from highly under-sampled …k -space data and the initial MRI image. Our algorithm can establish a one-to-one correspondence between the two imaging modalities, and obtain a good initial MRI estimation. Both noise-free and noisy simulation studies were performed to evaluate and validate the proposed algorithm. The results with different under-sampling factors show that the proposed algorithm performed significantly better than those reconstructed using the DL algorithm from MRI data alone. Show more

Keywords: MRI-CT Image reconstruction, dictionary learning (DL), dual-dictionary learning (DDL), multimodality

DOI: 10.3233/XST-160540

Citation: Journal of X-Ray Science and Technology, vol. 24, no. 1, pp. 161-175, 2016