Abstract #2801

AUTOMAP Image Reconstruction of Low Signal-to-Noise MR Data at 6.5 mT

Neha Koonjoo^1,2,3, Bo Zhu^1,2,3, and Matthew S Rosen^1,2,3

¹Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Physics, Harvard University, Cambridge, MA, United States

Due to very low Boltzmann polarization, MR images acquired at ultra-low field (ULF), MR images are mostly corrupted with noise, thus resulting in low signal-to-noise. In the aim of improving image quality at ULF, we apply the deep neural network image reconstruction technique, AUTOMAP, to low SNR datasets acquired at 6.5 mT. The performance of AUTOMAP (Automated Transform by Manifold Approximation) versus the conventional Inverse Fast Fourier Transform (IFFT) on this data was evaluated. The results for AUTOMAP reconstruction show a significant noise reduction, leading to more than 30% gain in signal to noise ratio as compared to standard IFFT.

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