Deep Learning in MR Image Processing

Doohee Lee, Jingu Lee, Jingyu Ko, Jaeyeon Yoon, Kanghyun Ryu, Yoonho Nam
2019 Investigative Magnetic Resonance Imaging  
Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion,
more » ... nd image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications. 82 Deep Learning in MRI | Doohee Lee, et al. directions of deep learning in MR image processing. Deep Learning: a Brief Overview Deep learning is a branch of machine learning based on the use of multiple layers to learn data representations, and can be applied to both supervised and unsupervised learning (11). These multiple layers allow the machine to learn multiple level features of data in order to achieve its desired function. Figure 1a presents a simplified version of a neural network, which has been the most widely used deep learning architecture over the last decade. Each layer of deep learning architecture consists of a set of nodes, and each node is represented by a digitized number.
doi:10.13104/imri.2019.23.2.81 fatcat:txjrlwhklbh47nxbwiq55xkhva