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Tofu is a toolkit for processing large amounts of images and for tomographic reconstruction. Complex image processing tasks are organized as workflows of individual processing steps. The toolkit is able to reconstruct parallel and cone beam as well as tomographic and laminographic geometries. Many pre- and post-processing algorithms needed for high-quality 3D reconstruction are available, e.g. phase retrieval, ring removal and de-noising. Tofu is optimized for stand-alone GPU workstations on which it achieves reconstruction speed comparable with costly CPU clusters. It automatically utilizes all GPUs in the system and generates 3D reconstruction code with minimal number of instructions given the input geometry (parallel/cone beam, tomography/laminography), hence yielding optimal run-time performance. In order to improve accessibility for researchers with no previous knowledge of programming, tofu contains graphical user interfaces for both optimization of 3D reconstruction parameters and batch processing of data with pre-configured workflows for typical computed tomography reconstruction. The toolkit is open source and extensive documentation is available for both end-users and developers. Thanks to the mentioned features, tofu is suitable for both expert users with specialized image processing needs (e.g. when dealing with data from custom-built computed tomography scanners) and for application-specific end-users who just need to reconstruct their data on off-the-shelf hardware.

An array of compound refractive X-ray lenses (CRL) with 20x20 lenslets, a focal distance of 20 cm and a visibility of 0.93 is presented. It can be used as a Shack-Hartmann sensor for hard X-rays (SHARX) for wavefront sensing and permits for true single-shot multi-contrast imaging the dynamics of materials with a spatial resolution in the micrometer range, sensitivity on nanosized structures and temporal resolution on the microsecond scale. The object's absorption and its induced wavefront shift can be assessed simultaneously together with information from diffraction channels. This enables the imaging of hierarchical materials. In contrast to the established Hartmann sensors the SHARX has an increased flux efficiency through focusing of the beam rather than blocking parts of it. We investigated the spatiotemporal behavior of a cavitation bubble induced by laser pulses. Furthermore, we validated the SHARX by measuring refraction angles of a single diamond CRL, where we obtained an angular resolution better than 4 microrad.