Abstract #1758

A Deep-Learning Framework for Image Reconstruction of Undersampled and Motion-Corrupted k-space Data

Nalini M Singh^1,2, Juan Eugenio Iglesias^1,3,4, Elfar Adalsteinsson^2,5,6, Adrian V Dalca^1,4, and Polina Golland^1,5,6

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, ²Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Centre for Medical Image Computing, University College London, London, United Kingdom, ⁴A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ⁵Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁶Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States

We propose a deep learning approach for reconstructing undersampled k-space data corrupted by motion. Our algorithm achieves high-quality reconstructions by employing a novel neural network architecture that captures the correlation structure jointly present in the frequency and image spaces. This method provides higher quality reconstructions than techniques employing solely frequency space or solely image space operations. We further characterize the motion severities for which the proposed method is successful. This analysis represents the first step toward fast image reconstruction in the presence of substantial motion, such as in pediatric or fetal imaging.

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