Member-Initiated Tutorial: Cardiovascular MR: From Theory to Practice II
Tutorial
Thursday, 20 May 2021
| Concurrent 7 |
18:00 - 19:00 |
Moderators: |
Organizers: Teresa Correia & Thomas Küstner
Overview
Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiac anatomy, function, perfusion, and viability. However, diversity and complexity of imaging and reconstruction methods pose some limitations to the widespread use of CMR. Therefore, it is necessary to bridge the gap between the clinical and scientific communities.
This tutorial will cover in four sessions different aspects of CMR:
1) CMR methods for identifying cardiovascular disease with a case-based tutorial;
2) Conduction and planning of a complete CMR exam;
3) Parametric mapping and functional imaging techniques with a live computational MR physics tutorial; and
4) Image reconstruction and analysis strategies with a live deep learning tutorial.
Sessions 1 and 2 will provide a clinical background while sessions 3 and 4 cover state-of-the-art methods and current research developments including recent advances in the field of deep learning. Sessions 3 and 4 will host a code challenge for the audience to participate in.
Further information on the challenge is available at: https://ismrm-mit-cmr.github.io/
Awards will be announced on Thursday, May 20th.
Target Audience
Physicians, radiographers, physicists, and engineers who wish to understand the main clinical applications of CMR, acquisition, and reconstruction methods. The tutorial is suitable for beginners in the field and for more advanced users to refresh or deepen their understanding on different aspects of CMR.
Educational Objectives
Upon completion of this course, participants should be able to:
- Explain basic cardiac anatomy, cardiovascular diseases, and its characterization by CMR and other diagnostic tools;
- Describe the basic CMR examination and recognize the advantages and limitations of the technique;
- Explain the basics of multiparametric and quantitative imaging; and
- Explain the basics of deep learning-based methods for image reconstruction and analysis.
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