Fig.5 Testing of the final model on a new subject randomly chosen from the 7T HCP database. Shown are mean diffusion-weighted images with b1000 (averaged across all directions) and color-coded FA maps in sagittal, coronal and axial views. The final model with tuned hyperparameters was trained on our entire dataset of 5 subjects. Note that the final model (ResNet) substantially enhanced the image quality by restoring signal dropout observed in the lower brain regions (as marked by yellow arrowheads), producing color-coded FA maps with reduced noise levels in those challenging regions.

Fig.3 Example b0 and b1000 diffusion images (of one diffusion direction) for single transmission (sTx) vs deep-learned pTx (ResNet) in reference to acquired pTx images. Shown is a representative axial slice in lower brain from one subject, in which case the model with tuned hyperparameters was trained on data of the other 4 subjects. Note that the use of ResNet substantially improved the image quality, effectively recovering the signal dropout observed in the lower temporal lobe (as marked by the yellow arrowheads) and producing images that were comparable to those obtained with pTx.