Abstract #3514

Patch-based AUTOMAP image reconstruction of low SNR 1.5 T human brain MR k-space

Neha Koonjoo^1,2, Bo Zhu^1,2, Danyal Bhutto^1,2,3, Suresh E Joel⁴, and Matthew S Rosen^1,2,5

¹Department of Radiology, A.A Martinos Center for Biomedical Imaging / MGH, Charlestown, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Department of Biomedical Engineering, Boston University, Boston, MA, United States, ⁴GE Healthcare, Bangalore, India, ⁵Department of Physics, Harvard University, Cambridge, MA, United States

Synopsis

AUTOMAP has proved itself to be robust to noise especially in the low SNR regimes, however due to the fully connected architecture of the input layer, its applicability to large matrix size datasets has been limited. Here we propose a patched-based trained network that enables the reconstruction of larger datasets. Low SNR single-channel volume coil brain images were acquired at 1.5T with different pulse sequences and reconstructed with the trained model. Results obtained show significant denoising potential. An increase in SNR of 1.5-fold as well as an increase in SSIM was also observed.

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