Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling

Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. In this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. We believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Kees Joost Batenburg,et al.  Dynamic angle selection in binary tomography , 2013, Comput. Vis. Image Underst..

[4]  Peter A. J. Hilbers,et al.  A Bayesian approach to targeted experiment design , 2012, Bioinform..

[5]  Hyrum S. Anderson,et al.  Sparse imaging for fast electron microscopy , 2013, Electronic Imaging.

[6]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[7]  Charles A. Bouman,et al.  A model-based framework for fast dynamic image sampling , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[9]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[10]  Jian Zhou,et al.  Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction , 2014, ICML.

[11]  Wen-Huang Cheng,et al.  Computer-aided classification of lung nodules on computed tomography images via deep learning technique , 2015, OncoTargets and therapy.

[12]  Ryutarou Ohbuchi,et al.  Quasi-Monte Carlo rendering with adaptive sampling , 1996 .

[13]  Manolis Kellis,et al.  Deep learning for regulatory genomics , 2015, Nature Biotechnology.

[14]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[15]  Leslie J. Allen,et al.  Atomic-resolution chemical mapping using energy-dispersive x-ray spectroscopy , 2010 .

[16]  Dong Hye Ye,et al.  Dynamic X-ray diffraction sampling for protein crystal positioning. , 2017, Journal of synchrotron radiation.

[17]  Ge Wang,et al.  A Perspective on Deep Imaging , 2016, IEEE Access.

[18]  Charles A. Bouman,et al.  A Supervised Learning Approach for Dynamic Sampling , 2016, Computational Imaging.

[19]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[20]  Alberto Eljarrat,et al.  hyperspy: HyperSpy 0.8 , 2015 .

[21]  Joseph I. Goldstein,et al.  Scanning Electron Microscopy and X-Ray Microanalysis: A Text for Biologists, Materials Scientists, and Geologists , 1981 .

[22]  Ondrej L. Krivanek,et al.  Single atom identification by energy dispersive x-ray spectroscopy , 2012 .

[24]  D. Rowenhorst,et al.  Particle coarsening in high volume fraction solid-liquid mixtures , 2006 .

[25]  Jelena Kovacevic,et al.  An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets , 2005, IEEE Transactions on Image Processing.

[26]  Matthias W. Seeger,et al.  Compressed sensing and Bayesian experimental design , 2008, ICML '08.

[27]  Charles A. Bouman,et al.  A Framework for Dynamic Image Sampling Based on Supervised Learning , 2017, IEEE Transactions on Computational Imaging.

[28]  A. Robert Calderbank,et al.  Communications-Inspired Projection Design with Application to Compressive Sensing , 2012, SIAM J. Imaging Sci..

[29]  Zhongmin Wang,et al.  Variable density compressed image sampling , 2009, 2009 17th European Signal Processing Conference.

[30]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Bernhard Schölkopf,et al.  Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design , 2010, Magnetic resonance in medicine.

[32]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.