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The emergence of commercial electron backscatter diffraction (EBSD) equipment ushered in an era of information rich maps produced by determining the orientation of user-selected crystal structures. Since then, a technological revolution has occurred in the quality, rate detection, and analysis of these diffractions patterns. The next revolution in EBSD is the ability to directly utilize the information rich diffraction patterns in a high-throughput manner. Aided by machine learning techniques, this new methodology is, as demonstrated herein, capable of accurately separating phases in a material by crystal symmetry, chemistry, and even lattice parameters with fewer human decisions. This work is the first demonstration of such capabilities and addresses many of the major challenges faced in modern EBSD. Diffraction patterns are collected from a variety of samples, and a convolutional neural network, a type of machine learning algorithm, is trained to autonomously recognize the subtle differences in the diffraction patterns and output phase maps of the material. This study offers a path to machine learning coupled phase mapping as databases of EBSD patterns encompass an increasing number of the possible space groups, chemistry changes, and lattice parameter variations.

Automated phase maps are an important tool for characterizing samples but the data quality must be evaluated. Common options include the overlay of phases on backscattered electron (BSE) images and phase composition averages and standard deviations. Both these methods have major limitations. We propose two methods of evaluation involving principal component analysis. First, a red–green–blue composite image of the first three principal components, which comprise the majority of the chemical variation, which provides a good reference against which phase maps can be compared. Advantages over a BSE image include discriminating between similar mean atomic number phases and sensitivity across the entire range of mean atomic numbers present in a sample. Second, principal component maps for identified phases, to examine for chemical variation within phases. This ensures the identification of unclassified phases and provides the analyst with information regarding the chemical heterogeneity of phases (e.g., chemical zoning within a mineral or mineral chemistry changing across an alteration zone). Spatial information permits a good understanding of heterogeneity within a phase and allows analytical artifacts to be easily identified. These methods of evaluation were tested on a complex geological sample. K-means clustering and K-nearest neighbor algorithms were used for phase classification, with the evaluation methods demonstrating their limitations.

[EN] Rotor-guided ablation has opened new perspectives into the therapy of atrial fibrillation (AF). Analysis of the spatio-temporal cardiac excitation patterns in the frequency and phase domains has demonstrated the importance of rotors in research models of AF, however, the dynamics and role of rotors in human AF are still controversial. In this review, the current knowledge gained through research models and patient data that support the notion that rotors are key players in AF maintenance is summarized. We report and discuss discrepancies regarding rotor prevalence and stability in various studies, which can be attributed in part to methodological differences among mapping systems. Future research for validation and improvement of current clinical electrophysiology mapping technologies will be crucial for developing mechanistic-based selection and application of the best therapeutic strategy for individual AF patient, being it, pharmaceutical, ablative, or other approach., This work was supported in part by grants from the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-01882, PI13-00903, and PI14/00857), Spanish Society of Cardiology (Clinical Research Grant 2015), Generalitat Valenciana (ACIF/2013/021), Innovation (Red RIC, PLE2009-0152), and NHLBI (P01-HL039707, P01-HL087226, and R01-HL118304).

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Extended abstract of a paper presented at Microscopy and Microanalysis 2009 in Richmond, Virginia, USA, July 26 – July 30, 2009

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

In conventional x-ray mapping, energy regions are defined to straddle the N x-ray spectral peaks of interest. At each pixel position in the digital image, counts are recorded from each energy region and thus give rise to N element maps of intensity distributions over the field of view. Fig. 1 shows an example set of x-ray maps from a polished test sample. It is clear from the images that there are circular regions of differing composition but it is not immediately obvious which regions have the same composition. Furthermore, in a real sample, phases are unlikely to appear in such regular patterns so the correlations between maps are in general much more difficult to notice.It is possible to choose two x-ray maps and generate a 2D scatter plot showing the intensity values in both maps for all pixels in the image.