Organizers

Co-Chairs:
Thomas Küstner, Ph.D.
Bruno Madore, Ph.D.

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Workshop Overview

Motion during MRI is an ongoing problem but also presents an opportunity to characterize dynamic processes or utilize this information in other applications and methods. This workshop will cover advances in motion detection, prospective and retrospective corrections, registration, motion modeling, image reconstruction techniques handling motion, and deep learning solutions within this context. Possible themes include: tracking (optical, field cameras, navigators), fetal imaging, neurological motion, motion modeling for therapy planning, signal modeling (for fingerprinting or multi-parametric imaging), motion as an opportunity (cardiac, bowel motility, speech and swallowing, fetal motion, motor experiments, and fMRI), and data-driven/deep learning techniques to identify, characterize, or utilize motion.

Target Audience

This workshop is aimed at three distinct groups:

• New MR scientists who would like to get hands-on experience and a good introduction to various motion correction techniques;
• Experienced MR scientists currently developing motion correction techniques and interested in alternative solutions and ongoing discussions about motion correction solutions; and
• Clinicians/radiographers interested in applying the latest correction methods to solve their motion problems.

Educational Objectives

Upon completion of this activity, participants should be able to:

• Explain the underlying causes, challenges, and pitfalls of motion artifacts in MRI;
• List the methods currently applied to prevent and correct for motion effects in (i) clinical MRI and (ii) research;
• Describe current work performed to develop new motion detection, prevention and correction methods, and the feasibility of a clinical translation;
• Examine hands-on experiences of available motion correction techniques; and
• Identify the potentials and challenges of machine learning for motion detection and correction and future research directions.

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