Deep Learning for Magnetic Resonance Image Reconstruction and Analysis
Chen Qin

Seminar • 06 August 2019

Abstract
Recent advances in deep learning have shown great potentials in improving the entire medical imaging pipeline, from image acquisition and reconstruction to disease diagnosis. In this talk, I will mainly focus on Magnetic Resonance (MR) image reconstruction and analysis. Firstly, I will introduce my recent study on dynamic MR image reconstruction from highly undersampled k-space data. A CRNN (convolutional recurrent neural network) model will be presented where it models the traditional iterative optimisation process in a learning setting and is able to exploit the spatio-temporal redundancies effectively and efficiently. As a complement, a k-t NEXT (k-t Network with X-f Transform) method will be introduced in which image signals are recovered by alternating the reconstruction process between x-f space and image space in an iterative fashion. Secondly, I will briefly present our recent research on parallel MRI reconstruction, where variable splitting idea is adopted and is modeled in a deep learning framework. Besides, some works on MRI analysis directly from undersampled data will also be presented, including cardiac segmentation and motion estimation, where we showed that prediction directly from undersampled MRI can still achieve accurate performance, potentially enabling fast analysis for MR imaging.