Abstract #4432

Highly accelerated and motion-robust 2D TSE brain MRI: Combining SAMER retrospective MoCo with a data-driven regularizer

Rodrigo Andujar Lugo^1,2, Yannick Juli², Dominik Nickel², Bryan Clifford³, Daniel N Splitthoff², Wei-Ching Lo³, Susie Y. Huang⁴, John Conklin⁴, Lawrence L. Wald⁵, Steven Cauley³, and Daniel Polak²

¹Friedrich-Alexander University, Erlangen, Germany, ²Siemens Healthineers AG, Forcheim, Germany, ³Siemens Medical Solutions USA, Boston, MA, United States, ⁴Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, ⁵Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States

Synopsis

Keywords: Motion Correction, Motion Correction, Deep Learning, Accelerated Imaging, Neuroimaging

Motivation: To meet the clinical demand for fast and motion-robust brain MRI.

Goal(s): To integrate retrospective motion correction with a data-driven deep learning reconstruction method to achieve high-quality, motion-robust 2D TSE imaging.

Approach: Motion trajectory information was derived from scout and guidance line-based motion correction. A data-driven deep learning network was developed, interleaving multiple conjugate gradient SENSE (+motion) optimizations with network regularization, and was trained and evaluated on TSE data.

Results: The method demonstrated improved signal-to-noise ratio (SNR) and reduced motion artifacts in vivo, utilizing 4-fold accelerated scans with induced step motion.

Impact: We integrate retrospective motion correction into a data-driven deep learning network to facilitate fast and motion-robust 2D TSE imaging in the brain.

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