Abstract #0386

AI-based motion estimation in k-space using guidance lines enables scoutless prospective motion correction.

Julian Hossbach^1,2, Daniel Splitthoff², Bryan Clifford³, Daniel Polak^2,4, Wei-Ching Lo³, Stephen Cauley³, Tobias Kober⁵, Min Lang^4,6, Azadeh Tabari^4,6, Jeremy Ford^4,6, Komal Manzoor^4,6, Lawrence Wald^4,6,7, Otto Rapalino^4,6, Pamela Schaefer^4,6, John Conklin^4,6, Susie Huang^4,6,7, Heiko Meyer², and Andreas Maier¹

¹Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, ²Siemens Healthcare GmbH, Erlangen, Germany, ³Siemens Medical Solutions, Boston, MA, United States, ⁴Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States, ⁵Advanced Clinical Imaging Technology Group, Siemens Healthcare International AG, Lausanne, Switzerland, ⁶Harvard Medical School, Boston, MA, United States, ⁷Harvard-MIT Health Sciences and Technology, Boston, MA, United States

Synopsis

Keywords: Motion Correction, Motion Correction, Prospective, motion estimation

Motivation: Novel research reduced the acquisition of motion navigators to a few guidance lines. A prospective correction is not yet possible due to reconstruction and optimization times.

Goal(s): We determine the feasibility of a fast AI-based motion estimation for prospective correction in a 3D MPRAGE research sequence.

Approach: A DL network to prospectively estimate the head pose from seamlessly integrated guidance lines in a 3D MPRAGE research sequence was trained on simulated data and used for a rapid adaption of the FOV to achieve a prospective correction.

Results: In-vivo experiments showed greatly reduced motion artifacts. The motion estimation is accurate and stable.

Impact: Prospectively adapting the FOV using the proposed AI-based method greatly improves the image quality of 3D MPRAGE acquisitions. This unique application of ML enables promising research of prospectively mitigating motion artifacts with minimal changes to the sequence.

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.