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Visualisation & Quantification of Tomographic Datasets Training Course
September 16 @ 9:00 am - 5:00 pm
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A 2 day training course covering theory and computer-based practical works using the Thermo Scientific Amira-Avizo Software.
Venue: Rutherford Appleton Laboratory Visitors Centre at RAL, on the Harwell Campus, Oxfordshire.
COST £400 + VAT for academics, students, postdocs & government, £800 + VAT for industry.
Lab/synchrotron X-ray tomography has emerged as one of the most important techniques for research in a wide range of applications including healthcare, energy, food and geology. Advances in the quality of X-ray beams, optics, high speed data acquisition and in-situ environments have made significant improvements to non-destructive imaging of a specimen structure in 3D and over a wide range of length scales (micron-and nanoscales). Although the direct benefit of X-ray tomography is the 3D visualisation of the internal structure of specimens, which is very valuable to understand the structure/function relationships, the technique has however a lot more to offer. The information is hidden in the data and robust methods and workflows are needed to extract it in a relevant and accurate way, and make it readily available for decision making.
This training course is organised by The University of Manchester at Harwell and 3Dmagination Ltd. It is aimed at researchers working in the field of mechanics, physics, chemistry, biology and materials science using 3D imaging techniques such as X-ray computed tomography, and who are interested to acquire solid methods for visualising and extracting relevant scientific information from 3D tomographic data.
The training is organised in two parts: theory (0.5 day) and computer-based practical works using the Thermo Scientific Amira-Avizo Software 3D (1.5 day). At the end of the second day, the users are invited to practise on their own dataset with the help of the trainers. The attendees are invited to bring their own laptop (an Avizo license will be provided for the training).
Courses generally fill up very quickly and places are allocated on a first come first serve basis. If there is low attendance, a course may be cancelled with a month’s notice.
