Martijn Froeling1
1Imaging and oncology, University medical center utrecht, Utrecht, Netherlands
1Imaging and oncology, University medical center utrecht, Utrecht, Netherlands
For
accurate fitting of muscle diffusion tensor imaging data, many methods have
been proposed. In this study, the performance of a physics-informed deep
learning method for IVIM-DTI fitting is investigated. Such networks are capable
of fitting the model within seconds per dataset.
Figure 1:
The design of the self-learning physics informed neural net. Top: a single
encoder for each parameter, which consists of 4 fully connected layers with ELU
activation layers that end in a sigmoid activation layer to constrain the
parameters. Bottom: The full neural net of which the inputs, i.e. the b-matrix
and the voxel signal vector, are indicated in orange. The encoders estimate the
parameter vector which is then used in the signal generator. The difference
between the generated signal and the input signal is minimized by a mean
squared loss layer.
Figure 2:
Parameter maps obtained from the IVIM-DTI model of one volunteer. The parameter
maps look similar but subtle differences can be seen. Especially the signal
IVIM perfusion fraction is more homogeneous and higher when obtained with the
NN.