FDuDoCLNet: Fully dual-domain contrastive learning network for parallel MRI reconstruction

Huiyao Zhang; Tiejun Yang; Heng Wang; Jiacheng Fan; Wenjie Zhang; Mingzhu Ji

doi:10.1016/j.mri.2025.110336

FDuDoCLNet: Fully dual-domain contrastive learning network for parallel MRI reconstruction

Magn Reson Imaging. 2025 Jan 24:110336. doi: 10.1016/j.mri.2025.110336. Online ahead of print.

Authors

Huiyao Zhang¹, Tiejun Yang², Heng Wang¹, Jiacheng Fan¹, Wenjie Zhang¹, Mingzhu Ji¹

Affiliations

¹ School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
² School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control (HAUT), Ministry of Education, Zhengzhou, China; Henan Key Laboratory of Grain Photoelectric Detection and Control (HAUT), Zhengzhou, Henan, China. Electronic address: tjyanghlyu@126.com.

PMID: 39864600
DOI: 10.1016/j.mri.2025.110336

Abstract

Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that is widely used for high-resolution imaging of soft tissues and organs. However, the slow speed of MRI imaging, especially in high-resolution or dynamic scans, makes MRI reconstruction an important research topic. Currently, MRI reconstruction methods based on deep learning (DL) have garnered significant attention, and they improve the reconstruction quality by learning complex image features. However, DL-based MR image reconstruction methods exhibit certain limitations. First, the existing reconstruction networks seldom account for the diverse frequency features in the wavelet domain. Second, existing dual-domain reconstruction methods may pay too much attention to the features of a single domain (such as the global information in the image domain or the local details in the wavelet domain), resulting in the loss of either critical global structures or fine details in certain regions of the reconstructed image. In this work, inspired by the lifting scheme in wavelet theory, we propose a novel Fully Dual-Domain Contrastive Learning Network (FDuDoCLNet) based on variational networks (VarNet) for accelerating PI in both the image and wavelet domains. It is composed of several cascaded dual-domain regularization units and data consistency (DC) layers, in which a novel dual-domain contrastive loss is introduced to optimize the reconstruction performance effectively. The proposed FDuDoCLNet was evaluated on the publicly available fastMRI multi-coil knee dataset under a 6× acceleration factor, achieving a PSNR of 34.439 dB and a SSIM of 0.895.

Keywords: Contrastive learning; Deep learning; MRI reconstruction; Parallel imaging.