Divya Varadarajan1,2, Katie Bouman3, Bruce Fischl*1,2,4, and Adrian Dalca*1,5
1Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, United States, 4Massachusetts General Hospital, Boston, MA, United States, 5Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States
1Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, United States, 4Massachusetts General Hospital, Boston, MA, United States, 5Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States
We propose an unsupervised
deep-learning strategy that generalizes over multiple acquisition parameters and employs the FLASH MRI model to jointly estimate T1,
T2* and PD tissue parameter maps with the goal to synthesize physically
plausible FLASH signals.
Figure 1: Proposed
framework: The proposed model to synthesize arbitrary FLASH MRI contrasts using
a CNN and a FLASH forward model from three input image contrasts. As a
consequence of using the FLASH model, the output of the CNN can be interpreted
as estimates of the tissue parameters (T1,T2* and PD).
Figure
2: Contrast Synthesis: Test error in synthesis of image contrasts estimated
from three input images over 100 test datasets.
The boxplots in 2a and 2b, plot
the MAE and the images in 2c. show the reference, proposed estimate and
fixed acq. network estimate. The images in 2c. show a slice from the
test dataset, with the reference estimated from 3 flip 4 echo scan and
predicted contrast from both random and fixed networks.