Abstract #1371

Motion-Aware Neural Networks Improve Rigid Motion Correction of Accelerated Segmented Multislice MRI

Nalini M. Singh^1,2, Malte Hoffmann^3,4, Elfar Adalsteinsson^2,5,6, Bruce Fischl^2,3,4, Polina Golland*^1,5,6, Adrian V. Dalca*^1,3,4, and Robert Frost*^3,4

¹Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT, Cambridge, MA, United States, ²Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States, ³Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ⁴Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁵Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States, ⁶Institute for Medical Engineering and Science, MIT, Cambridge, MA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Motion Correction, Image Reconstruction, Deep LearningWe demonstrate a deep learning approach for fast retrospective intraslice rigid motion correction in segmented multislice MRI. A hypernetwork uses auxiliary rigid motion parameter estimates to produce a reconstruction network based on the motion parameters that are specific to the input image. This strategy produces higher quality reconstructions than those produced by model-based techniques or by networks that do not use motion estimates. Further, this approach mitigates sensitivity to misestimation of the motion parameters.

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