A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction

Understand

Decreasing magnetic resonance (MR) image acquisition times can potentially reduce procedural cost and make MR examinations more accessible.

• Compressed sensing (CS)-based image reconstruction methods, for example, decrease MR acquisition time by reconstructing high-quality images from data that were originally sampled at rates inferior to the Nyquist-Shannon sampling theorem.
• In this work we propose a hybrid architecture that works both in the k-space (or frequency-domain) and the image (or spatial) domains.
• Our network is composed of a complex-valued residual U-net in the k-space domain, an inverse Fast Fourier Transform (iFFT) operation, and a real-valued U-net in the image domain.