Abstract #4780

AUTOMAP Image Reconstruction of Ultra-Low Field Human Brain MR Data

Neha Koonjoo ^1,2,3, Bo Zhu^1,2,3, Matthew Christensen^1,2, John E. Kirsch^1,2, and Matthew S Rosen^1,2,3

¹Department of Radiology, A.A Martinos Biomedical Imaging Center / MGH, Charlestown, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Department of Physics, Harvard University, Cambridge, MA, United States

Due to very low Boltzmann polarization, MR images acquired at ultra-low field (ULF), MR images require significant signal averaging to overcome low signal-to-noise, which results in longer scan times. Here, we apply the deep neural network image reconstruction technique, AUTOMAP (Automated Transform by Manifold Approximation), to 50% under-sampled low SNR in vivo datasets acquired at 6.5 mT. The performance of AUTOMAP on this data was compared to the conventional 3D Inverse Fast Fourier Transform (IFFT). The results for AUTOMAP reconstruction show a significant improvement in image quality and SNR.

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