Super-resolution MRI through Deep Learning

Understand

Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics.

• Due to hardware, physical and physiological limitations, acquisition of high-resolution MRI data takes long scan time at high system cost, and could be limited to low spatial coverage and also subject to motion artifacts.
• Super-resolution MRI can be achieved with deep learning, which is a promising approach and has a great potential for preclinical and clinical imaging.
• Compared with polynomial interpolation or sparse-coding algorithms, deep learning extracts prior knowledge from big data and produces superior MRI images from a low-resolution counterpart.