Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data

Hyungjin Chung, Eunju Cha, Leonard Sunwoo, Jong Chul Ye

Abstract:

Time-of-flight magnetic resonance angiography (TOF-MRA) is one of the most widely
used non-contrast MR imaging methods to visualize blood vessels, but due to the 3-
D volume acquisition highly accelerated acquisition is necessary. Accordingly, high
quality reconstruction from undersampled TOF-MRA is an important research topic for
deep learning. However, most existing deep learning works require matched reference
data for supervised training, which are often difficult to obtain. By extending the recent
theoretical understanding of cycleGAN from the optimal transport theory, here we propose a novel two-stage unsupervised deep learning approach, which is composed of the
multi-coil reconstruction network along the coronal plane followed by a multi-planar
refinement network along the axial plane. Specifically, the first network is trained in the
square-root of sum of squares (SSoS) domain to achieve high quality parallel image reconstruction, whereas the second refinement network is designed to efficiently learn the
characteristics of highly-activated blood flow using double-headed max-pool discriminator. Extensive experiments demonstrate that the proposed learning process without
matched reference exceeds performance of state-of-the-art compressed sensing (CS)-
based method and provides comparable or even better results than supervised learning
approaches.