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Tomography

Online | 13 Dec 2021

The aim of this course is to provide in 1 day a short overview of the possibilities of Xray tomography (in large scale instruments and laboratories) with some knowledge on data acquisition and data analysis. After this course you will be able to plan a tomography experiments on your specific material either on lab tomograph or with large scale instruments and perform qualitative and quantitative with numerical tools. The course is divided in two sessions of 3 hours: the first session is dedicated to theoretical knowledge and the second session to practice. In the second session you will use the python to understand the main important parameters to perform a tomography experiments and Fiji for the data analysis. 

Session 1: Theoretical courses  (2h)

1) Tomography: general principles (45 min)
1.1) General principles
1.2) X ray tomography (laboratory and synchrotron sources)
1.3) Applications


2) Data acquisition and reconstruction: (45 min)
2.1) Main important parameters (energy, resolution, sample size, scan time)
2.2) Large scale instruments tomography and Laboratory tomography, in situ 
2.3) Main tomography mode (absorption, phase contrast, holotomography)
2.4) Reconstruction (classical FBP method, algebraic method, IA)


3) Data Analysis: (30 min) 
3.1) 3D qualitative analysis : 2D and 3D inspection.
3.2) 3D quantitative analysis :  filtering, segmentation and parameters extraction
3.3) Software for data analysis

Session 2: Labs sessions (3h)

 

1) Experience preparation, data acquisition and reconstruction (1h30)

1.1) Xray attenuation coefficient
1.2) Xray spectrum and Selection of energy and filter
1.3) Reconstruction (FBP, SIRT, IA)
1.4) Novisim


2) Data analysis with Fiji and python (1h30)
2.1) 2D data visualisation
2.2) 3D data Filtering 3D 
2.3) Data segmentation and 3D visualisation
2.4) 3D quantitative analysis (parameters measurements, filtering)


Prequisites: Basic knowledge of tomography

Contacts: Pierre Lhuissier: pierre.lhuissier@simap.grenoble-inp.fr Luc Salvo: luc.salvo@grenoble-inp.fr