Meeting Banner
Abstract #0354

SISMIK: Search In Segmented Motion Input in K-space

Oscar Albert Dabrowski1, Jean-Luc Falcone1, Antoine Klauser1, Julien Songeon1, Michel Kocher2, Bastien Chopard1, Francois Lazeyras1, and Sebastien Courvoisier1
1University of Geneva, Geneva, Switzerland, 2EPFL, Geneva, Switzerland

Synopsis

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, k-space, motion, artifacts, quality metric

Motivation: Motion correction in MRI predominantly relies on image-based methods and continues to be a challenge. Innovative approaches could harness better motion information latent in k-space (i.e., the measurement space).

Goal(s): Developing a reference-less motion correction pipeline in k-space using deep learning.

Approach: Our k-space motion correction pipeline combines deep learning for motion parameter estimation with model-based image reconstruction. Large datasets were generated through physics-based simulations on 2D brain MRI acquisitions to enhance model training and performance.

Results: Our deep-learning model performs well in motion parameter estimation, even for successive motion events, effectively removing substantial motion artifacts when combined with model-based reconstruction.

Impact: SISMIK, our deep learning model successfully estimates motion parameters in the acquisition space of multi-slice 2D brain MRI. It allows substantial motion artifact removal through a model-based reconstruction approach, which is, by design, free of hallucination artifacts.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords