DDC-Net: Dual-Domain Cascaded Network With POCS Prior for Fast MRI Reconstruction

作者：Zhijie Wang, Aiping Liu, Jinbao Wei, Qingguo Xie, Kongqiao Wang, Xun Chen · 发表于：IEEE Sensors Journal · 年份：2024 · DOI：10.1109/jsen.2024.3366764 · 被引用次数：10 · 研究领域：Advanced MRI Techniques and Applications、Medical Imaging Techniques and Applications、Atomic and Subatomic Physics Research

Magnetic resonance imaging (MRI) is a powerful diagnostic tool that provides high-resolution images, but it requires a time-consuming scanning procedure. To reduce the acquisition time, various methods have been proposed to reconstruct images from undersampled${k}$-space. However, most dual-domain learning-based methods have two primary limitations: 1) using the same reconstruction network for different domains may hinder the learning of targeted information for each domain and 2) the reconstruction network uses only highly undersampled data as input, leaving the use of prior as complementary information highly underexplored. To alleviate the above issues, we propose a dual-domain cascaded network with projection onto convex set (POCS) prior for fast MRI, called DDC-Net. It consists of cascaded blocks with image-domain subnetwork (I-Net) and frequency-domain subnetwork (K-Net) reconstruction subnetworks. Specifically, the customized I-Net for the image domain uses a two-encoder-one-decoder architecture to enrich detailed structures. On the other hand, the customized K-Net for the frequency (${k}$-space) domain replaces the traditional encoder–decoder with a cross-domain encoder–decoder to alleviate the difficult modeling problem of${k}$-space data. In addition, the POCS prior as complementary information is deeply embedded in the dual domain to simultaneously guide the${k}$-space and image reconstructions. Extensive experiments on the fastMRI and CC359 datasets demonstrate th...