Image Quality Transfer in MRI with Deep Neural Networks

Image Quality Transfer (IQT) is a machine learning based framework to propagate rich information in high-quality but expensive images to low-quality clinical data. We study the application of IQT to enhancement of human brain MR images. In specific, this requires solving two problems: super-resolution inferring sub-voxel structures and contrast enhancement between anatomical structures. In this project, we will practically explore deep learning algorithms on MRI data. The project will involve testing the popular network architectures [1-2] on publicly available MRI database and studying the image quality for epilepsy detection.


[1] Tanno, Ryutaro, et al. “Bayesian image quality transfer with CNNs: exploring uncertainty in dMRI super-resolution.” MICCAI 2017.

[2] Heinrich, Larissa, John A. Bogovic, and Stephan Saalfeld. “Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy.” MICCAI 2017.