Deep plug-and-play MRI reconstruction based on multiple complementary priors

Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method has become an important technical means to accelerate MRI by reducing the data sampling rate while maintaining high-quality imaging. However, traditional undersampling reconstruction techniques such as compressed sensing mainly rely on relatively single sparse or low-rank prior information to reconstruct the image, which has limitations in capturing the comprehensive features of images, resulting in the insufficient performance of the reconstructed image in terms of details and key information. In this paper, we propose a deep plug-and-play multiple complementary priors MRI reconstruction model, which combines traditional low-rank matrix recovery model methods and deep learning methods, and integrates global, local and nonlocal priors to improve reconstruction quality. Specifically, we capture the global features of the image through the matrix nuclear norm, and use the deep convolutional neural network denoiser Swin-ConvUNet (SCUNet) and block-matching and 3-D filtering (BM3D) algorithm to preserve the local details and structural texture of the image, respectively. In addition, we utilize an efficient half-quadratic splitting (HQS) algorithm to solve the proposed model. The experimental results show that our proposed method has better reconstruction ability than the existing popular methods in terms of visual effects and numerical results.