Lightweight brain MR image super-resolution using 3D convolution

Magnetic resonance imaging (MRI) plays a very important role in a medical domain, such as image guided diagnostics and therapeutics. In particular, high resolution brain MRI has a great potential for preclinical and clinical procedures, because it is non-invasive imaging and shows a high level of anatomical detail. However, the high resolution MRI faces a number of challenges, such as long scan time, high magnetic field strength, and low signal to noise ratio. To solve these issues, deep learning based super-resolution networks, which provide high performance in various fields, can be employed in MRI. Since the super-resolution networks have been mainly developed to reconstruct high quality color images by using many parameters, they cannot be directly applied into MR scanners. Hence, this paper evaluates conventional networks with brain MR images, and then proposes a lightweight network employing 3D convolution, which consists of extraction, compression, and reconstruction parts. Experimental results show that the proposed network is very efficient, in terms of reconstruction quality and network complexity.