The aim of this study was to optimise the experimental protocol and data analysis for in-vivo breast cancer x-ray imaging. Results are presented of the experiment at the SYRMEP beamline of Elettra Synchrotron using the propagation-based phase-contrast mammographic tomography method, which incorporates not only absorption, but also x-ray phase information. In this study the images of breast tissue samples, of a size corresponding to a full human breast, with radiologically acceptable x-ray doses were obtained, and the degree of improvement of the image quality (from the diagnostic point of view) achievable using propagation-based phase-contrast image acquisition protocols with proper incorporation of x-ray phase retrieval into the reconstruction pipeline was investigated. Parameters such as the x-ray energy, sample-to-detector distance and data processing methods were tested, evaluated and optimized with respect to the estimated diagnostic value using a mastectomy sample with a malignant lesion. The results of quantitative evaluation of images were obtained by means of radiological assessment carried out by 13 experienced specialists. A comparative analysis was performed between the x-ray and the histological images of the specimen. The results of the analysis indicate that, within the investigated range of parameters, both the objective image quality characteristics and the subjective radiological scores of propagation-based phase-contrast images of breast tissues monotonically increase with the strength of phase contrast which in turn is directly proportional to the product of the radiation wavelength and the sample-to-detector distance. The outcomes of this study serve to define the practical imaging conditions and the CT reconstruction procedures appropriate for low-dose phase-contrast mammographic imaging of live patients at specially designed synchrotron beamlines.

X-ray computed tomography (CT) experiments performed at synchrotron radiation facilities require adequate computing and storage resources due to the large amount of acquired and reconstructed data produced. To satisfy the heterogeneous needs of beamline users, flexible solutions are also required. Moreover, the growing demand of quantitative image analysis impose an easy integration between the CT reconstruction process and the subsequent feature extraction step. This paper presents some of the software solutions adopted by the SYRMEP beamline of the Italian synchrotron radiation facility Elettra. By using the enhanced version of the reconstruction software here presented as well as data reduction and data analysis tools, beamline users can easily implement an integrated and comprehensive approach to the digital image processing and image analysis required by a tomography-oriented scientific workflow

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Histology and immunohistochemistry of thin tissue sections have been the standard diagnostic procedure in many diseases for decades. This method is highly specific for particular tissue regions or cells, but mechanical sectioning of the specimens is required, which destroys the sample in the process and can lead to non-uniform tissue deformations. In addition, regions of interest cannot be located beforehand and the analysis is intrinsically two-dimensional. Micro X-ray computed tomography (μCT) on the other hand can provide 3D images at high resolution and allows for quantification of tissue structures, as well as the localization of small regions of interest. These advantages advocate the use of μCT for virtual histology tool with or without subsequent classical histology. This review summarizes the most recent examples of virtual histology and provides currently known possibilities of improving contrast and resolution of μCT. Following a background in μCT imaging, ex vivo staining procedures for contrast enhancement are presented as well as label-free virtual histology approaches and the technologies, which could rapidly advance it, such as phase-contrast CT. Novel approaches such as zoom tomography and nanoparticulate contrast agents will also be considered. The current evidence suggests that virtual histology may present a valuable addition to the workflow of histological analysis, potentially reducing the workload in pathology, refining tissue classification, and supporting the detection of small malignancies.