Extracting diffusion tensor fractional anisotropy and mean diffusivity from 3-direction DWI scans using deep learning.

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Abstract

To develop and evaluate machine-learning methods that reconstruct fractional anisotropy (FA) values and mean diffusivities (MD) from 3-direction diffusion MRI (dMRI) acquisitions.
Two machine-learning models were implemented to map undersampled dMRI signals with high-quality FA and MD maps that were reconstructed from fully sampled DTI scans. The first model was a previously described multilayer perceptron (MLP), which maps signals and FA/MD values from a single voxel. The second was a convolutional neural network U-Net model, which maps dMRI slices to full FA/MD maps. Each method was trained on dMRI brain scans (N = 46), and reconstruction accuracies were compared with conventional linear-least-squares (LLS) reconstructions.
In an independent testing cohort (N = 20), 3-direction U-Net reconstructions had significantly lower absolute FA error than both 3-direction MLP (U-Net3-dir : 0.06 ± 0.01 vs. MLP3-dir : 0.08 ± 0.01, P < 1 × 10-5 ) and 6-direction LLS (LLS6-dir : 0.09 ± 0.03, P = 1 × 10-5 ). The MD errors were not significantly different among 3-direction MLP (0.06 ± 0.01 × 10-3 mm2 /s), 3-direction U-Net (0.06 ± 0.01 × 10-3 mm2 /s), and 6-direction LLS (0.07 ± 0.02 × 10-3 mm2 /s, P > .1).
The proposed U-Net model reconstructed FA from 3-direction dMRI scans with improved accuracy compared with both a previously described MLP approach and LLS fitting from 6-direction scans. The MD reconstruction accuracies did not differ significantly between reconstructions.
© 2020 International Society for Magnetic Resonance in Medicine.