  In this paper, we propose a method for denoising diffusion-weighted images
(DWI) of the brain using a convolutional neural network trained on realistic,
synthetic MR data. We compare our results to averaging of repeated scans, a
widespread method used in clinics to improve signal-to-noise ratio of MR
images. To obtain training data for transfer learning, we model, in a
data-driven fashion, the effects of echo-planar imaging (EPI): Nyquist ghosting
and ramp sampling. We introduce these effects to the digital phantom of brain
anatomy (BrainWeb). Instead of simulating pseudo-random noise with a defined
probability distribution, we perform noise scans with a brain-DWI-designed
protocol to obtain realistic noise maps. We combine them with the simulated,
noise-free EPI images. We also measure the Point Spread Function in a DW image
of an AJR-approved geometrical phantom and inter-scan movement in a brain scan
of a healthy volunteer. Their influence on image denoising and averaging of
repeated images is investigated at different signal-to-noise ratio levels.
Denoising performance is evaluated quantitatively using the simulated EPI
images and qualitatively in real EPI DWI of the brain. We show that the
application of our method allows for a significant reduction in scan time by
lowering the number of repeated scans. Visual comparisons made in the acquired
brain images indicate that the denoised single-repetition images are less noisy
than multi-repetition averaged images. We also analyse the convolutional neural
network denoiser and point out the challenges accompanying this denoising
method.
