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    Based on our experience and customers’ needs, we offer 2 training courses to extract relevant scientific information from tomographic data, with a good balance between theory and practice, using world-wide imaging and modelling softwares such as Amira-Avizo Software provided by Thermo Fisher Scientific.

     

    We also provide custom-built training adapted to your needs and applications.

     

    We would love to meet you in person and learn about your work. Please contact us for any advice or to book a training at kamel.madi@3dmagination.eu. 

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    Course 1 : Visualisation and quantification of tomographic data 

    Learning objectives: overview of the X-ray CT imaging chain with guidelines to produce images of good quality, full quantification/visualisation of tomographic data and the impact of imaging noise and artefacts

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    The training is organised in two parts: theory (0.5 day) and computer-based practical works using the Amira-Avizo software (1.5 day). Coffee and refreshments are included each day.

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    Day 1

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    9.00-12.30: Introduction to X-ray CT

    • Production of X-ray

    • Interaction of X-ray with matter

    • Sinogram and CT reconstruction

    • CT artefacts

    • Practical guide to optimise imaging conditions

    • Assessment and tutorials to validate the learning objectives:

      • Socrative quiz (theory)

      • Learn how CT reconstruction works (software)

      • Case studies to learn how to optimise imaging parameters (tune spectrum, filters and materials using bespoke Excel macro)

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    13.30-17.30: Practical works 

    • Basics of image processing (voxel, structural element, neighbourhood, morphological tools, etc.)

    • Introduction to Avizo software

    • Import/manipulate 3D dataset

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    Day 2

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    9.00-12.30: Practical works

    • 3D visualisation based on grey-level (3D surface/volume rendering)

    • Filters

    • Basic and advanced segmentation (interactive thresholding, top-hat, watershed, region growing, etc.)

    • Quantification (particle size distribution, shape analysis, etc.)

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    13.30-17.30: Practical works

    • Registration of 3D dataset

    • Animation and movies

    • Final session: the users are invited to practise on their own dataset with the help of the trainers

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    Course 2 : In situ experiments and Digital Volume Correlation (DVC) 

    Learning objectives: Digital Volume Correlation (DVC) has recently emerged as a powerful contactless, bulk strain measurement technique in experimental mechanics, materials science, and biomechanics. Learn about the importance of imaging noise and texture when using  DVC (displacement/strain accuracy, texture analysis, subset size, etc.), understand how to perform a DVC analysis, analyse and visualise the data in a sophisticated way.

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    The training is organised in two parts: theory (0.5 day) and computer-based practical works using Amira-Avizo software for Digital Volume Correlation (1.5 day): 

     

    Coffee and refreshments are included each day.

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    Day 1:

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    9.00-12.30: Theory

    • In situ experiments coupled with X-ray CT

    • Optimise your experiment prior DVC

    • Introduction to Digital Volume Correlation

      • Principle

      • Texture, microstructure length scale

      •  Importance of noise and artefacts

      • Link with FE modelling 

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    13.30-17.30: Practical works

    • Introduction to Amira-Avizo software for DVC

    • Understand and exploit the texture using morphological tools

    • Quantify displacement/strain accuracy of real dataset

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    Day 2: 

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    9.00-12.30: Real case studies taken from our experience

    • Learn how to combine DVC with advanced image processing and visualise the data in a sophisticated way

      • Application 1: Investigate strain transfer, cracking and phase transformation in two-phase materials

      • Application 2: Learn how to couple DVC with micro-FE modelling   

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    13.30-17.30: Real case studies taken from user experience

    • Bring your own dataset and practise with the trainers to extract valuable information

    Avizo-DVC_Pore-Coalescence_Web

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    Courtesy of L. Laiarinandrasana & T. Morgeneyer (Centre des Matériaux Mines ParisTech), and L. Helfen (European Synchrotron Radiation Facility; Karlsruhe Institute of Technology)

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