Methodology development and application of X-ray imaging beamline at SSRF

This paper introduces some latest developments regarding the X-ray imaging methodology and applications of the X-ray imaging and biomedical application beamline(BL13W1)at Shanghai Synchrotron Radiation Facility in the past 5 years.The photon energy range of the beamline is 8–72.5 keV.Several sets of X-ray imaging detectors with different pixel sizes(0.19–24 lm)are used to realize X-ray microcomputed tomography(X-ray micro-CT)and X-ray in-line phase-contrast imaging.To satisfy the requirements of user experiments,new X-ray imaging methods and image processing techniques are developed.In vivo dynamic micro-CT experiments with living insects are performed in 0.5 s(sampling rate of 2 Hz,2 tomograms/s)with a monochromatic beam from a wiggler source and in 40 ms(sampling rate of 25 Hz,25 tomograms/s)with a white beam from a bending magnet source.A new X-ray imaging method known as move contrast X-ray imaging is proposed,with which blood flow and moving tissues in raw images can be distinguished according to their moving frequencies in the time domain.Furthermore,X-ray speckle-tracking imaging with twice exposures to eliminate the edge enhancement effect is developed.A high-precision quantification method is realized to measure complex three-dimensional blood vessels obtained via X-ray micro-CT.X-ray imaging methods such as three-dimensional X-ray diffraction microscopy,small-angle X-ray scattering CT,and X-ray fluorescence CT are developed,in which the X-ray micro-CT imaging method is combined with other contrast mechanisms such as diffraction,scattering,and fluorescence contrasts respectively.Moreover,an X-ray nano-CT experiment is performed with a 100 nm spatial resolution.Typical user experimental results from the fields of material science,biomedicine,paleontology,physics,chemistry,and environmental science obtained on the beamline are provided.

X-ray fluorescence microtomography based on polycapillary- focused X-rays from laboratory source

X-ray fluorescence microtomography(μXFCT)is a nondestructive analytical technique and has been widely used to nondestructively detect and quantify the elemental composition and distributions in samples. Usually, synchrotron radiation X-rays are used for μXFCT, due to its high flux density. In this paper, a laboratory-sourcebased μXFCT system was developed, in which a polycapillary lens is employed to focus the X-ray beam and improve the flux density. The maximum likelihood expectation maximization algorithm was used to reconstruct the computed tomography slices at a limited number of projections. The experimental results demonstrated that the developed system could reveal the elemental distribution inside the test sample, with an elemental sensitivity of 1000 ppm.

Multi-modality measurement and comprehensive analysis of hepatocellular carcinoma using synchrotron-based microscopy and spectroscopy

The visualization and data mining of tumor multidimensional information may play a major role in the analysis of the growth,metastasis,and microenvironmental changes of tumors while challenging traditional imaging and data processing techniques.In this study,a general trans-scale and multi-modality measurement method was developed for the quantitative diagnosis of hepatocellular carcinoma(HCC)using a combination of propagation-based phase-contrast computed tomography(PPCT),scanning transmission soft X-ray microscopy(STXM),and Fourier transform infrared micro-spectroscopy(FTIR).Our experimental results reveal the trans-scale micro-morpho-logical HCC pathology and facilitate quantitative data analysis and comprehensive assessment.These results include some visualization features of PPCT-based tissue microenvironments,STXM-based cellular fine structures,and FTIR-based bio-macromolecular spectral characteris-tics during HCC tumor differentiation and proliferation.The proposed method provides multidimensional feature data support for constructing a high-accuracy machine learning algorithm based on a gray-level histogram,gray-gradient co-occurrence matrix,gray-level co-occurrence matrix,and back-propagation neural network model.Multi-dimensional information analysis and diagnosis revealed the morphological pathways of HCC pathological evolution and we explored the relationships between HCC-related feature changes in inflammatory microenviron-ments,cellular metabolism,and the stretching vibration peaks of biomolecules of lipids,proteins,and nucleic acids.Therefore,the proposed methodology has strong potential for the visualization of complex tumors and assessing the risks of tumor differentiation and metastasis.