Abstract #0074

Network Accelerated Motion Estimation and Reduction (NAMER): Accelerating forward model based retrospective motion correction using a convolutional neural network

Melissa W. Haskell^1,2, Stephen F. Cauley^1,3, Berkin Bilgic^1,3, Julian Hossbach⁴, Josef Pfeuffer⁴, Kawin Setsompop^1,3,5, and Lawrence L. Wald^1,3,5

¹A.A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ²Program in Biophysics, Harvard University, Cambridge, MA, United States, ³Harvard Medical School, Boston, MA, United States, ⁴Siemens Healthineers AG, Erlangen, Germany, ⁵Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA, United States

Retrospective motion correction techniques have the potential to improve clinical imaging without altering the workflow or acquisition sequence. Yet, they suffer from long reconstruction times and poor conditioning. To address these problems, we developed a Network Accelerated Motion Estimation and Reduction method (NAMER) within a data-consistency based forward model approach to motion parameter estimation. The neural net accelerates convergence up to 15-fold as well as improving final image quality. The ML+MR physics motion correction method combines the speedup provided by fast convolutional neural networks with the robustness of a forward model-based data-consistency reconstruction.

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