ForMAX - a beamline for multiscale and multimodal structural characterization of hierarchical materials.

The ForMAX beamline at the MAX IV Laboratory provides multiscale and multimodal structural characterization of hierarchical materials in the nanometre to millimetre range by combining small- and wide-angle X-ray scattering with full-field microtomography. The modular design of the beamline is optimized for easy switching between different experimental modalities. The beamline has a special focus on the development of novel fibrous materials from forest resources, but it is also well suited for studies within, for example, food science and biomedical research.

Scanning Compton X-ray microscopy.

X-ray microscopy offers the opportunity to image biological and radiosensitive materials without special sample preparations, bridging optical and electron microscopy capabilities. However, the performance of such microscopes, when imaging radiosensitive samples, is not limited by their intrinsic resolution, but by the radiation damage induced on such samples. Here, we demonstrate a novel, to the best of our knowledge, radio-efficient microscope, scanning Compton X-ray microscopy (SCXM), which uses coherently and incoherently (Compton) scattered photons to minimize the deposited energy per unit of mass for a given imaging signal. We implemented SCXM, using lenses capable of efficiently focusing 60 keV X-ray photons into the sub-micrometer scale, and probe its radio-efficient capabilities. SCXM, when implemented in high-energy diffraction-limited storage rings, e.g., European Synchrotron Radiation Facility Extremely Brilliant Source and PETRA IV, will open the opportunity to explore the nanoscale of unstained, unsectioned, and undamaged radiosensitive materials.

Assessing lesion malignancy by scanning small-angle x-ray scattering of breast tissue with microcalcifications.

Scanning small-angle x-ray scattering (SAXS) measurements were performed on 36 formalin-fixed breast tissue biopsies obtained from two patients. All samples contained microcalcifications of type II, i.e. formed by hydroxyapatite. We demonstrate the feasibility of classifying breast lesions by scanning SAXS of tissues containing microcalcifications with a resolution of 35 [Formula: see text]m [Formula: see text] 30 [Formula: see text]m. We report a characteristic Bragg peak found around q = 1.725 nm-1 that occurs primarily for malignant lesions. Such a clear SAXS fingerprint is potentially linked to structural changes of breast tissue and corresponds to dimensions of about 3.7 nm. This material property could be used as an early indicator of malignancy development, as it is readily assessed by SAXS. If this fingerprint is combined with other known SAXS features, which also indicate the level of malignancy, such as lipid spacing and collagen periodicity, it could complement traditional pathology-based analyses. To confirm the SAXS-based classification, a histopathological workup and a gold standard histopathological diagnosis were conducted to determine the malignancy level of the lesions. Our aim is to report this SAXS fingerprint, which is clearly related to malignant breast lesions. However, any further conclusion based on our dataset is limited by the low number of patients and samples. Running a broad study to increase the number of samples and patients is of great importance and relevance for the breast-imaging community.

Model-free classification of X-ray scattering signals applied to image segmentation.

In most cases, the analysis of small-angle and wide-angle X-ray scattering (SAXS and WAXS, respectively) requires a theoretical model to describe the sample's scattering, complicating the interpretation of the scattering resulting from complex heterogeneous samples. This is the reason why, in general, the analysis of a large number of scattering patterns, such as are generated by time-resolved and scanning methods, remains challenging. Here, a model-free classification method to separate SAXS/WAXS signals on the basis of their inflection points is introduced and demonstrated. This article focuses on the segmentation of scanning SAXS/WAXS maps for which each pixel corresponds to an azimuthally integrated scattering curve. In such a way, the sample composition distribution can be segmented through signal classification without applying a model or previous sample knowledge. Dimensionality reduction and clustering algorithms are employed to classify SAXS/WAXS signals according to their similarity. The number of clusters, i.e. the main sample regions detected by SAXS/WAXS signal similarity, is automatically estimated. From each cluster, a main representative SAXS/WAXS signal is extracted to uncover the spatial distribution of the mixtures of phases that form the sample. As examples of applications, a mudrock sample and two breast tissue lesions are segmented.