Meeting Banner
Abstract #3908

Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network

Chao Li1, Hang Zhang1, Jinwei Zhang1, Pascal Spincemaille1, Thanh Nguyen1, and Yi Wang1
1Cornell university, New York, NY, United States


An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles to simulate motion-corrupted QSM images. The simulated QSM image is paired with its motion-free reference to train a neural network using supervised learning. The trained network is tested on unseen simulated motion-corrupted QSM images, in healthy volunteers and in Parkinson’s disease patients. The results show that motion artifacts, such as ringing and ghosting, were successfully suppressed.

This abstract and the presentation materials are available to members only; a login is required.

Join Here