The vertical intensity distribution of synchrotron-based X-ray beams usually has a Gaussian profile encompassing large intensity variations. For biomedical imaging applications this may entail sub-optimal dose distributions and large fluctuations in terms of image noise. Commonly, planar metallic filters coupled with absorbing slits systems are applied to adjust the delivered flux and to limit intensity variations, respectively. The latter results in a reduction of the effective beam size.  https://www.selleckchem.com/products/aminooxyacetic-acid-hemihydrochloride.html  A flattening filter that counterbalances the transverse inhomogeneity, while retaining a sufficient flux, has been developed in the context of a monochromatic phase-contrast breast computed tomography application, ongoing at the Elettra synchrotron facility. The implementation of the new filtration system results in homogeneous intensity (hence dose) distribution and signal-to-noise ratio across the imaged volume. Finally, and most importantly, it allows a wider portion of the beam to be used, directly translating into a major (∼40%) reduction of the overall scan time for samples requiring a field of view larger than the beam size (i.e. multiple translation steps).A three-image algorithm is proposed to retrieve the sample's transmission, refraction and dark-field information in hard X-ray grating interferometry. Analytical formulae of the three-image algorithm are theoretically derived and presented, and evaluated by proof-of-principle synchrotron radiation experiments. The results confirm the feasibility of the proposed algorithm. The novelty of the proposed algorithm is that it allows versatile and tunable multimodal X-ray imaging by substantially relaxing the existing limitations on the lateral grating position. Furthermore, this algorithm can also be adapted for samples with negligible refraction, reducing the number of required sample measurements to two. Furthermore, the noise properties of the retrieved images are investigated in terms of the standard deviations. Theoretical models are presented and verified by synchrotron radiation measurements. It is shown that the noise standard deviations exhibit strong dependence on the lateral grating position, especially in the case of refraction and dark-field images. Further noise reduction and dose reduction can thus be possible by optimizing the lateral grating position for a selected region of interest. Those results can serve as general guidelines to optimize the data acquisition scheme for specific applications and problems.This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180° rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70° could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging. open access.In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts because of missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. In particular, U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100° limited angle tomography. For synthetic test data, U-Net significantly reduces the root-mean-square error (RMSE) from 2.55 × 10-3 µm-1 in the FBP reconstruction to 1.21 × 10-3 µm-1 in the U-Net reconstruction and also improves the structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least-square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 × 10-3 µm-1 and 0.932, respectively. For real test data, the proposed method remarkably improves the 3D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nanoscale imaging in biology, nanoscience and materials science. open access.A simple two-spindle based lathe system for the preparation of cylindrical samples intended for X-ray tomography is presented. The setup can operate at room temperature as well as under cryogenic conditions, allowing the preparation of samples down to 20 and 50 µm in diameter, respectively, within minutes. Case studies are presented involving the preparation of a brittle biomineral brachiopod shell and cryogenically fixed soft brain tissue, and their examination by means of ptychographic X-ray computed tomography reveals the preparation method to be mainly free from causing artefacts. Since this lathe system easily yields near-cylindrical samples ideal for tomography, a usage for a wide variety of otherwise challenging specimens is anticipated, in addition to potential use as a time- and cost-saving tool prior to focused ion-beam milling. Fast sample preparation becomes especially important in relation to shorter measurement times expected in next-generation synchrotron sources. open access.