This paper describes a novel method for fast online analysis of X-ray Laue spots taken by means of an energy-dispersive X-ray 2D detector. Current pnCCD detectors typically operate at some 100 Hz (up to a maximum of 400 Hz) and have a resolution of 384 × 384 pixels, future devices head for even higher pixel counts and frame rates.The proposed online data analysis is based on a computer utilizing multiple Graphics Processing Units (GPUs), which allow for fast and parallel data processing. Our multi-GPU based algorithm is compliant with the rules of stream-based data processing, for which GPUs are optimized. The paper's main contribution is therefore an alternative algorithm for the determination of spot positions and energies over the full sequence of pnCCD data frames. Furthermore, an improved background suppression algorithm is presented.The resulting system is able to process data at the maximum acquisition rate of 400 Hz. We present a detailed analysis of the spot positions and energies deduced from a prior (single-core) CPU-based and the novel GPU-based data processing, showing that the parallel computed results using the GPU implementation are at least of the same quality as prior CPU-based results. Furthermore, the GPU-based algorithm is able to speed up the data processing by a factor of 7 (in comparison to single-core CPU-based algorithm) which effectively makes the detector system more suitable for online data processing.

A: This paper presents a novel method for fast determination of absolute intensities in the sites of Laue spots generated by a tetragonal hen egg-white lysozyme crystal after exposure to white synchrotron radiation during an energy-dispersive X-ray Laue diffraction experiment. The Laue spots are taken by means of an energy-dispersive X-ray 2D pnCCD detector. Current pnCCD detectors have a spatial resolution of 384 × 384 pixels of size 75 × 75 µm 2 each and operate at a maximum of 400 Hz. Future devices are going to have higher spatial resolution and frame rates.The proposed method runs on a computer equipped with multiple Graphics Processing Units (GPUs) which provide fast and parallel processing capabilities. Accordingly, our GPU-based algorithm exploits these capabilities to further analyse the Laue spots of the sample. The main contribution of the paper is therefore an alternative algorithm for determining absolute intensities of Laue spots which are themselves computed from a sequence of pnCCD frames. Moreover, a new method for integrating spectral peak intensities and improved background correction, a different way of calculating mean count rate of the background signal and also a new method for n-dimensional Poisson fitting are presented.We present a comparison of the quality of results from the GPU-based algorithm with the quality of results from a prior (base) algorithm running on CPU. This comparison shows that our algorithm is able to produce results with at least the same quality as the base algorithm. Furthermore, the GPU-based algorithm is able to speed up one of the most time-consuming parts of the base algorithm, which is n-dimensional Poisson fitting, by a factor of more than 3. Also, the entire procedure of extracting Laue spots' positions, energies and absolute intensities from a raw dataset of pnCCD frames is accelerated by a factor of more than 3.

No abstract

No abstract