The Swiss Light Source (SLS) has been operational now for nearly two decades. In this period, it has spearheaded groundbreaking research in biomedicine, engineering and the natural sciences, thanks in large part to the excellent performance of the underpinning electron accelerator and storage ring complex. In addition, it has led the world in industrial exploitation, particularly by the pharmaceutical sector, and spawned numerous new companies, including one of the most successful Swiss technology spinoffs, Dectris. For much of this time, the SLS was a benchmark with regards to how closely its performance matched the theoretical limits deﬁned by its machine parameters. However, with the advent of the next generation of synchrotron light sources, called diffraction-limited storage-rings (DLSRs), that yield an emittance and brightness improved by up to two orders of magnitude, it has become imperative to upgrade the SLS (called SLS 2.0) in like manner. It is planned to upgrade the machine in 2023/2024 with a planned improvement in performance of up to a factor of 40, and return to regular user operation in 2025.
In modern microscopy, the field of view is often increased by obtaining an image mosaic, where multiple sub-images are taken side-by-side and combined post-acquisition. Mosaic imaging often leads to long imaging times that can increase the probability of sample deformation during the acquisition due to, e.g. changes in the environment, damage caused by the radiation used to probe the sample or biologically induced deterioration. Here we propose a technique, based on local phase correlation, to detect the deformations and construct an artifact-free image mosaic from deformed sub-images. The implementation of the method supports distributed computing and can be used to generate teravoxel-size mosaics. We demonstrate its capabilities by assembling a 5.6 teravoxel tomographic image mosaic of microvasculature in whole mouse brain. The method is compared to existing rigid stitching implementations designed for very large datasets, and observed to create artifact-free image mosaics in comparable runtime with the same hardware resources.