Applications of Deep Learning in Magnetic Resonance-Based Image Processing

Shayan Nalbandian¹ Amirreza Rouhbakhsh Meghrazi²

1) Department of Software, Northwestern Polytechnical University, Xi’An, Shaanxi, China,
2) Department of Electronic Information, Northwestern Polytechnical University, Xi’An, Shaanxi, China, Email:

Publication : 2nd International Congress on Science, engineering & New Technologies(secongress.com)
Abstract :
In the realm of handling vast amounts of digital data, deep learning algorithms are proving to be highly effective and promising. This has led to their widespread adoption as a substitute or complement to traditional model-based techniques in MR imaging research. There have been remarkable achievements in a variety of domains within MR image processing, including image reconstruction, quality improvement, parameter mapping, contrast transformation, and segmentation. With the rapid advancements in deep learning technology, there is a growing significance for its role in MR imaging research. This informative article explores the fundamental principles of deep learning and showcases its recent progress in various applications for MR image processing.
Keywords : Deep Learning Image Processing Machine Learning Computer Vision Image Segmentation