Abstract #0016

Synthetic MRI through a Deep Neural Network Based Relaxometry and Segmentation

Peng Cao¹, Jing liu¹, Shuyu Tang¹, Andrew Leynes¹, and Peder Larson¹

¹University of California at San Francisco, University of California at San Francisco, San Francisco, CA, United States

This study demonstrated a method for 3D synthetic MRI through a deep neural network Based Relaxometry and Segmentation. Ranges of T1 and T2 values for gray matter, white matter and cerebrospinal fluid (CSF) were used as the prior knowledge. The proposed method can directly generate brain T1 and T2 maps in conjunction with segmentation based bias field correction and synthetic MRI.

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