Ultra-fast X-ray Imaging
The experimental station has been designed fast radiography, tomography and laminography imaging.
|Imaging geometry||Speed requirements||Resolution requirements|
|Fast radiography||≥ 100 kHz frame rate||≤ 1 µm|
|Fast tomography||≥ 100 Hz tomogram rate||≤ 1 µm|
|Laminography||≥ 1 Hz laminography||≤ 1 µm|
|Contrast method||Energy bandwidth dE/E|
|Absorption||10-1 filtered white beam|
|Propagation-based phase||10-2 multi-layer monochromator|
|Grating-based phase||10-2 multi-layer monochromator|
|Spectroscopic||10-4 chrystal monochromator|
|Sample size||≥ 30 mm|
|Sample change time||≤ 10 s/sample|
|Sample throughput||≥ 360 samples/h|
The detector system consists of four modules:
The detector is fully motorized, to setup any scintillator, optical, filter and camera configuration. The broad range of possible applications require different camera types for the detector system: an ultra-high-speed camera (with integration times down to 10 µs and frame rates of up to 100,000 frames/s), a high dynamic range camera (with a contrast ratio of 20.000:1 for high spatial resolution down to 1 µm) and a fully programmable high-throughput camera for data driven fast reject and on-line control tasks. The pco.edge was selected to cover the high dynamic range and high-resolution requirements, the pco.dimax is used for high frame rates. For the high-throughput streaming mode applications a custom detector is developed.
The UFO parallel computing framework organizes an image-processing task as a graph, that describes the data flowing from camera to storage. It uses OpenCL to exploit the parallel processing capabilities of current GPU devices.
All filter nodes that are necessary for the computation of the filtered back-projection were ported from the previous monolithic PyHST reconstruction software or re-implemented. The reconstruction step was optimized for different NVIDIA and ATI architectures.
Besides parallel-beam tomography, laminography has also been ported to OpenCL. To increase the quality of the reconstructed volume, a GPU version of the non-local means noise reduction filter was implemented. Filters and computation scripts were developed to compute interpolated dark- and flat-field corrected frames, the center of rotation and the correlation-based global shift. Furthermore, we provide a Python extension module to allow developers to use Numpy/Scipy packages for processing the data.
High-speed process control requires multiple features like fast interconnects, customizable image-based triggers and process control logic, which are not provided by commercial cameras. It is based on the CMOSIS active pixel sensor, mounted on a mezzanine daughter card. The daughter card is connected to the main readout board. The camera is seamlessly integrated in the UFO parallel processing framework. The main benefits of the high-throughput camera prototype are:
An image-based fast reject algorithm has been developed that triggers acquisition in situations with unpredictable event occurrence. It is used to increase the effective bandwidth and thus significantly increases the frame rate. The fast reject logic is able to use a row-based subsampling mechanism that drastically reduces the readout time.
The rows of the current and the previously stored reference frame are checked for differences. In case a meaningful difference is present, signal triggers are generated. The readout logic uses this trigger information to select the region to be read out.