Brain MRI motion artifact reduction using 3D conditional generative adversarial networks on simulated motion

Magnetic resonance imaging is a well established technique for clinical diagnosis and quantifying disease modifying therapies. Whilst the exquisite tissue contrast sensitivity of MRI is indisputable, the modality can be susceptible to motion artifacts occurring during the acquisition phase. In this work, we propose to use a 3D deep learning network based on conditional generative adversarial netwo...