Training a neural network for Gibbs and noise removal in diffusion MRI.
PURPOSE
To develop and evaluate a neural network-based method for Gibbs artifact and noise removal.


METHODS
A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images.


RESULTS
Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions.


CONCLUSIONS
The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
['Muckley|Matthew J|MJ|0000-0002-6525-8817', 'Ades-Aron|Benjamin|B|', 'Papaioannou|Antonios|A|', 'Lemberskiy|Gregory|G|', 'Solomon|Eddy|E|', 'Lui|Yvonne W|YW|', 'Sodickson|Daniel K|DK|', 'Fieremans|Els|E|', 'Novikov|Dmitry S|DS|0000-0002-4213-3050', 'Knoll|Florian|F|0000-0001-5357-8656']
D016477:Artifacts D038524:Diffusion Magnetic Resonance Imaging D007091:Image Processing, Computer-Assisted D008279:Magnetic Resonance Imaging D016571:Neural Networks, Computer
neural network diffusion MRI Gibbs ringing denoising