Joint Neural Network for Fast Retrospective Rigid Motion Correction of Accelerated Segmented Multislice MRI
Nalini M Singh1,2, Malte Hoffmann3,4, Daniel C Moyer1, Ikbeom Jang3,4, Lina Chen5, Marcio Aloisio Bezerra Cavalcanti Rockenbach5, Arnaud Guidon6, Iman Aganj3,4, Jayashree Kalpathy-Cramer3,4,5, Elfar Adalsteinsson2,7,8, Bruce Fischl2,3,4, Adrian V Dalca1,3,4, Polina Golland*1,7,8, and Robert Frost*3,4
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 3Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, 4Department of Radiology, Harvard Medical School, Boston, MA, United States, 5MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, MA, United States, 6GE Healthcare, Boston, MA, United States, 7Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 8Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
We demonstrate a deep learning approach for fast retrospective intraslice rigid motion correction in segmented multislice MRI. The proposed neural network architecture combines convolutions on frequency and image space representations of the acquired data to produce high quality reconstructions. Unlike many prior techniques, our method does not require auxiliary information on the subject head motion during the scan. The resulting reconstruction procedure is more accurate and is an order of magnitude faster than GRAPPA. Our work offers the first step toward fast motion correction in any setting with substantial, unpredictable, challenging to track motion.
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