Algorithm for phase contrast X-ray tomography based on nonlinear phase retrieval

A new algorithm for phase contrast X-ray tomography under holographic measurement was proposed in this paper. The main idea of the algorithm was to solve the nonlinear phase retrieval problem using the Newton iterative method. The linear equations for the Newton directions were proved to be ill-posed and the regularized solutions were obtained by the conjugate gradient method. Some numerical experiments with computer simulated data were presented. The efficiency, feasibility and the numerical stability of the algorithm were illustrated by the numerical experiments. Compared with the results produced by the linearized phase retrieval algorithm, we can see that the new algorithm is not limited to be only efficient for the data measured in the near-field of the Fresnel region and thus it has a broader validity range.

An Alternative Derivation for Bronnikov's Reconstruction Algorithm in X-ray Phase Contrast Tomography

In this article, we present an alternative derivation for Bronnikov's reconstruction algorithm in X-ray phase contrast tomography with holographic measurements. A two-step method was used in the alternative derivation. The phase shift induced by the object was obtained by Fourier transform and the real part of the complex refractive index of the object was retrieved by applying the conventional filtered backprojection method. The alternative derivation provides an easier way to understand the reconstruction formula.

Phase Contrast Micro-Computed Tomography of Biological Sample at SSRF

In line X-ray phase contrast micro-computed tomography （IL-XPCT）, which can be implemented at third generation synchrotron radiation sources or by using a micro-focus X-ray tube, is a powerful technique for non-destructive, high-resolution investigations of a broad variety of materials. At the Shanghai Synchrotron Radiation Facility （SSRF）, the X-ray Imaging and Biomedical Applications Beamline was built and started regular user operation in May 2009. Both qualitative （without phase retrieval） and quantitative （with phase retrieval） three-dimensional IL-XPCT experimental techniques have been established at the beamline IL-XPCT experiments of a test sample （plastic pipes） used to evaluate the technique, and of a biological sample （locust） at the beamline are reported. Two series of images, qualitative and quantitative, including tomographic slices and three-dimensional rendering images were obtained. In qualitative images, there is a strong edge-enhancement which leads to very clear sample contours, while in quantitative images, the edge-enhancement fades but quantitative measurement of sample＇s phase information could be achieved. The experiments demonstrate that the combination of qualitative and quantitative images is useful for biological sample studies.

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.

A case with insulinoma localized by combined use of various means

A patient with insulinoma diagnosed by clinical features and localized preoperatively using a combination of contrast-enhanced ultrasonography （CEUS）, dual phase contrast enhanced spiral computed tomography （DPSCT） and arterial stimulation and venous sampling （ASVS） was reported. A 37-year-old man was admitted to our hospital because of hypoglycemic attacks, palpitations, and muscular weakness. Fajans＇ ratio reported to be an index for insulinoma was positive. Transabdominal computed tomography and ultrasonography failed to detect any abnormalities. CEUS showed a small low echoic lesion in the pancreatic body with blood flow and the early arterial phase of DPSCT revealed a small strengthening focus, which mimicked a pancreatic tumor in the pancreatic body. ASVS showed that the insulin levels in the hepatic vein were extremely increased by calcium injection to the gastroduodenal artery. An open intra-abdominal operation was performed and an insulinoma was confirmed in the pancreatic body. Enucleation of tumor was undertaken and histopathological examination showed an adenoma, insulin expression was positive in immunofluorescence staining.Symptomatic hypoglycemia never happened even without glucose infusion since the operation. His blood glucose level improved to within the normal range.