Review: Noise and artifact reduction for MRI using deep learning
This is an incremental review paper that synthesizes prior work for researchers and practitioners in medical imaging, without introducing new methods.
The paper reviews existing deep learning methods for reducing noise and artifacts in MRI images, addressing challenges in clinical implementation due to the complexity of MRI mechanisms.
For several years, numerous attempts have been made to reduce noise and artifacts in MRI. Although there have been many successful methods to address these problems, practical implementation for clinical images is still challenging because of its complicated mechanism. Recently, deep learning received considerable attention, emerging as a machine learning approach in delivering robust MR image processing. The purpose here is therefore to explore further and review noise and artifact reduction using deep learning for MRI.