Hands-on Tutorial: Laying the Foundations of Cardiovascular MRI
Reconstruction
Sunrise Course
ORGANIZERS: Teresa
Correia, Christopher Nguyen, Tobias Wech
Tuesday, 13 May 2025
320
07:00 - 08:00
Moderators: Noriko
Oyama-Manabe & Maarten Terpstra
Skill Level: Basic
to Intermediate
Session Number: S-T-02
No CME/CE Credit
Session Number:
S-T-02
Overview
This hands-on tutorial will provide practical guidelines on how to
perform compressed sensing and AI-based reconstructions from accelerated
cardiovascular MRI acquisitions. This session is part of a series of 3
weekday lectures (1 weekday session, 2 hours) with 3 hands-on sunrise
sessions (3 x 1 hour) to provide practical demonstrations of key
concepts of cardiovascular MRI, including 1) clinical protocols for
identifying cardiovascular disease, 2) accelerated acquisition
strategies and corresponding reconstruction techniques, 3) automated
post-processing and reporting workflows. This tutorial will also
introduce the building blocks of machine learning and provide practical
examples of how these are used in cardiovascular MRI.
Weekday lectures:
1. Acquisition: Protocols and Planning I (35min)
2. Reconstruction: Faster and Better scans I (35min)
3. Post-processing & Analysis: Faster and Automated workflows I (35min)
4. Panel discussion (15min)
Sunrise sessions:
1. Acquisition: Protocols and Planning II (60min)
2. Reconstruction: Faster and Better scans II (60min)
3. Post-processing & Analysis: Faster and Automated workflows II (60min)
Target Audience
This tutorial is for physicians, radiographers, physicists, and
engineers who want to learn about accelerated cardiovascular MRI image
reconstruction methods. It is suitable for beginners and also for those
who want to brush up on their knowledge or explore advanced topics in
cardiovascular MRI.
Educational Objectives
As a result of attending this course, participants should be able to:
• Summarize rapid acquisition strategies and corresponding
reconstruction techniques for CMR;
• Demonstrate how to design code for compressed sensing image
reconstruction; and
• Demonstrate how to design code for AI-based image reconstruction.
07:00 |
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Introduction
Teo Lynette
Impact: Clinical perspective on how compressed sensing
and artificial intelligence-based reconstructions can fit
into clinical cardiac MRI workflow and the importance of
user engagement and addressing their concerns.
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07:30 |
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Reconstruction: Faster & Better Scans II
Thomas Küstner
Impact: In deep learning for MR image reconstruction,
regularization terms are learned and approximated by neural
networks. Image enhancement, direct mapping, physics-based
unrolling and distribution-based methods will be presented
and discussed.
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