Coherent X-ray imaging techniques, such as in-line holography, exploit the high brilliance provided by diffraction-limited storage rings to perform imaging sensitive to the electron density through contrast due to the phase shift, rather than conventional attenuation contrast. Thus, coherent X-ray imaging techniques enable high-sensitivity and low-dose imaging, especially for low-atomic-number (Z) chemical elements and materials with similar attenuation contrast. Here, the first implementation of in-line holography at the NanoMAX beamline is presented, which benefits from the exceptional focusing capabilities and the high brilliance provided by MAX IV, the first operational diffraction-limited storage ring up to approximately 300 eV. It is demonstrated that in-line holography at NanoMAX can provide 2D diffraction-limited images, where the achievable resolution is only limited by the 70 nm focal spot at 13 keV X-ray energy. Also, the 3D capabilities of this instrument are demonstrated by performing holotomography on a chalk sample at a mesoscale resolution of around 155 nm. It is foreseen that in-line holography will broaden the spectra of capabilities of MAX IV by providing fast 2D and 3D electron density images from mesoscale down to nanoscale resolution.

X-ray fluorescence microscopy performed at nanofocusing synchrotron beamlines produces quantitative elemental distribution maps at unprecedented resolution (down to a few tens of nanometres), at the expense of relatively long measuring times and high absorbed doses. In this work, a method was implemented in which fast low-dose in-line holography was used to produce quantitative electron density maps at the mesoscale prior to nanoscale X-ray fluorescence acquisition. These maps ensure more efficient fluorescence scans and the reduction of the total absorbed dose, often relevant for radiation-sensitive (e.g. biological) samples. This multimodal microscopy approach was demonstrated on human sural nerve tissue. The two imaging modes provide complementary information at a comparable resolution, ultimately limited by the focal spot size. The experimental setup presented allows the user to swap between them in a flexible and reproducible fashion, as well as to easily adapt the scanning parameters during an experiment to fine-tune resolution and field of view.

Time‐resolved three‐dimensional (3D) X‐ray imaging techniques rely on obtaining 3D information for each time point and are crucial for materials‐science applications in academia and industry. Standard 3D X‐ray imaging techniques like tomography and confocal microscopy access 3D information by scanning the sample with respect to the X‐ray source. However, the scanning process limits the temporal resolution when studying dynamics and is not feasible for many materials‐science applications, such as cell‐wall rupture of metallic foams. Alternatives to obtaining 3D information when scanning is not possible are X‐ray stereoscopy and multi‐projection imaging, but these approaches suffer from limited volumetric information as they only acquire a very small number of views or projections compared to traditional 3D scanning techniques. Here, we present optimized neural implicit X‐ray imaging (ONIX), a deep‐learning algorithm capable of retrieving a continuous 3D object representation from only a small and limited set of sparse projections. ONIX is based on an accurate differentiable model of the physics of X‐ray propagation. It generalizes across different instances of similar samples to overcome the limited volumetric information provided by limited sparse views. We demonstrate the capabilities of ONIX compared to state‐of‐the‐art tomographic reconstruction algorithms by applying it to simulated and experimental datasets, where a maximum of eight projections are acquired. ONIX, although it does not have access to any volumetric information, outperforms unsupervised reconstruction algorithms, which reconstruct using single instances without generalization over different instances. We anticipate that ONIX will become a crucial tool for the X‐ray community by (i) enabling the study of fast dynamics not possible today when implemented together with X‐ray multi‐projection imaging and (ii) enhancing the volumetric information and capabilities of X‐ray stereoscopic imaging.

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