Fundamentals of Musculoskeletal MRI II
Sunrise Course
ORGANIZERS: Rupsa Bhattacharjee, Tim Bray, Akshay Chaudhari, Feliks Kogan
Tuesday, 13 May 2025
313C
07:00 -
08:00
Moderators: Martijn Froeling & Rianne van der Heijden
Skill Level: Basic to Intermediate
Session Number: S-T-04
No CME/CE Credit
Session Number: S-T-04
Overview
The sunrise course series will provide an overview of fundamental MRI sequences that are used routinely in the clinic and in research studies, focusing on their diagnostic utility and approaches towards acceleration. The session will cover quantitative MRI approaches in different anatomies as well as best practices for the analysis of the acquired images using segmentation and shape analysis tools.
Target Audience
Students, post-doctoral fellows, and researchers interested in acquisition, reconstruction, and analysis techniques for morphological and quantitative MRI.
Educational Objectives
As a result of attending this course, participants should be able to:
• Describe the contrasts, speed, and utility of sequences used in clinical musculoskeletal imaging;
• Describe the mechanisms of quantitative imaging markers and techniques used in joints, the muscle, and skeleton;
• List best practices in the acquisition and fitting for quantitative musculoskeletal MRI techniques; and
• Discuss common image analysis methods that generate insights from images using segmentation and shape analysis tools.
| 07:00 | | Shape Analysis & its MSK Applications Valentina Pedoia |
| 07:20 | | Approaches to Automated Musculoskeletal Segmentation Anthony Gatti, test t Keywords: Musculoskeletal: Joints, Image acquisition: Machine learning This presentation will cover fundamentals and recent advances in automated MSK segmentation, touching on conventional supervised learning, open-source resources, and interactive foundation models aimed at accelerating segmentation. Common applications, and tips and tricks for automating segmentation will be provided. |
| 07:40 | | Best Practices for MSK qMRI Fitting & Visualization of Quantitative Data Christian Federau |