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Meeting 2014 TMS Annual Meeting & Exhibition
Symposium Data Analytics for Materials Science and Manufacturing
Presentation Title Model-based Iterative Reconstruction for Multimodal Electron Tomography
Author(s) Lawrence Drummy, Singanallur Venkatakrishnan, Marc DeGraef, Jeff Simmons, Charles Bouman
On-Site Speaker (Planned) Lawrence Drummy
Abstract Scope Electron tomography (ET) is a powerful tool for reconstruction of the 3D structure of materials to sub-nanometer resolution. Significant advances in ET instrumentation have been made in recent years, yet current reconstruction algorithms for inversion of the projection data do not properly model the image formation process and therefore yield poor results. Model Based Iterative Reconstruction (MBIR) provides a framework for tomographic reconstruction that incorporates a model for data acquisition and a model for the object to obtain reconstructions that are qualitatively superior to current methods such as Filtered Back Projection (FBP) and quantitatively accurate. Here we present a novel MBIR algorithm for multi-modal ET which accounts for the presence of anomalous measurements from Bragg scatter in the Bright Field (BF) data. MBIR results on simulated as well as real data show that the method can dramatically improve reconstructions of High Angle Annular Dark Field and BF-ET compared to FBP.
Proceedings Inclusion? Planned: A print-only volume

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A Markov Random Field Approach for Microstructure Synthesis
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A Novel Method for Automated Quantification of Particles in Solidified Aluminium
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Applications of Wavelets in the Representation and Prediction of Transformation in Shape-memory Polycrystals
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Bayesian Inference of Grain Boundary Properties from Heterogeneous Data
Comparison of Novel Microstructure Quantification Frameworks for Visualization, and Analysis of Microstructure Data
Compressed Sensing for Fast Electron Microscopy
Data Analysis and Quantification of 3D Microstructures
Data Analytics for Residual Stress in Materials
Data Topology as a Framework for Materials Discovery and Material Mimetic Design
Dictionary-based Diffraction Microscopy for Materials
Effective Extraction of Both Impurity Diffusion Coefficients and Interdiffusion Coefficients for Diffusivity Database Establishment
Forward Modeling of Electron Microscopy
Foundational Engineering Problem: Uncertainty Quantification in Multi-disciplinary Analysis of Bulk Residual Stresses in Disks
Fully Automated, High-throughput Powder X-ray Data Analysis
Grain Boundary Data as a Big Data Problem
Growth Path Envelope Analysis of Grain Growth in Tungsten
Hyperspectral Image Analysis: From Qualitative to Quantitative Analysis
Integrated Material Characterization Property Prediction Using 3D Image-based Analytics and Modeling
Leveraging Data Science to Enable Multiscale Materials Modeling and Design
Linking 3D X-ray Imaging and Simulations
Model-based Iterative Reconstruction for Multimodal Electron Tomography
Modeling Direct and Inverse Problems in Ferritic Heat-exchanger Tubes
New Data Mining Techniques in Materials Science : Bayesian Networks to Predict the Yield Stress of Ni-Base Superalloys
Not Your Father's Topology: Modern Views of Connectivity in Grain Structures
Phase-based Property Data Informatics
Physics-based Models for Information Processing with Applications to Materials Characterization
Physics of Regularized Image Processing
Predictive Modeling in Characterizing Localization Relationships
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Rapid Ideation, Modeling and Simulation in a Collaborative Crowdsourcing Environment for Evolutionary Design (CEED)
Scalable Graph-based Techniques for Large-scale Materials Data
Stochastic-integral Models for Propagation-of-uncertainty Problems in Nondestructive Evaluation
The Challenge of Combining Massive, High-dimensionality Data Streams from the Atomscope
The MGI and the Role of Theory
Toward the Minimal Set of Morphological Information for Statistical Material Microstructure Modeling
Virtual Analysis of Experimental Techniques for Determining Grain Volume Distribution and Number per Unit Volume

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