Patch-based AUTOMAP image reconstruction of low SNR 1.5 T human brain MR k-space
Neha Koonjoo1,2, Bo Zhu1,2, Danyal Bhutto1,2,3, Suresh E Joel4, and Matthew S Rosen1,2,5
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
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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