Undersampling raster scans in spectromicroscopy for a reduced dose and faster measurements

Abstract

Combinations of spectroscopic analysis and microscopic techniques are used across many disciplines of scientific research, including material science, chemistry and biology. X-ray spectromicroscopy, in particular, is a powerful tool used for studying chemical state distributions at the micro and nano scales. With the beam fixed, a specimen is typically rastered through the probe with continuous motion and a range of multimodal data is collected at fixed time intervals. The application of this technique is limited in some areas due to: long scanning times to collect the data, either because of the area/volume under study or the compositional properties of the specimen; and material degradation due to the dose absorbed during the measurement. In this work, we propose a novel approach for reducing the dose and scanning times by undersampling the raster data. This is achieved by skipping rows within scans and reconstructing the x-ray spectromicroscopic measurements using low-rank matrix completion. The new method is robust and allows for 5 to 6-fold reduction in sampling. Experimental results obtained on real data are illustrated.

Optimal Sparse Energy Sampling for X-ray Spectro-Microscopy: Reducing the X-ray Dose and Experiment Time Using Model Order Reduction

“…Random spatial subsampling at each energy can exploit the low number of unique chemical states in a real sample to complete missing information and provide a 5-to 6-fold reduction in dose and time. 7 These approaches are generalized and do not require or exploit any prior knowledge of the system. Knowledge of the possible chemical states should allow for optimized experiment design, significantly reducing the sampling conditions and the dose, and in the best case, the number of energy samples will approach the number of unique chemical states.…”

“…Novel artificial intelligence (AI)-driven scanning using route optimization has been shown to reduce overall time by sparsely sampling and inpainting to reduce measurement time down to 10% in some cases; while this is a promising direction, the application to XANES imaging has not yet been demonstrated. Random spatial subsampling at each energy can exploit the low number of unique chemical states in a real sample to complete missing information and provide a 5- to 6-fold reduction in dose and time . These approaches are generalized and do not require or exploit any prior knowledge of the system.…”