Simultaneous Multi-Slice MRI Reconstruction Using Outline Information and Multiple Variable Density Sampling

In Magnetic Resonance Imaging (MRI), Simultaneous Multi-Slice (SMS) using parallel reconstruction has become a major image technique to accelerate data acquisition. In these reconstructions, it is crucial to mitigate the inter-slice aliasing caused by SMS and the artifacts due to undersampling in parallel MRI (pMRI). In this study, we proposed to incorporate the outline information present in different slices to address inter-slice aliasing in SMS data. Additionally, the Multiple Variable Density Sampling (MVDS) method was proposed in SMS to reduce the undersampling-induced aliasing artifacts. This approach enhances image quality by utilizing sampling schemes with multiple reduction factors. Furthermore, the outline information and MVDS can be used jointly. In an experiment, the proposed reconstruction methods were compared with advanced algorithms such as SPSG, SMS-COOKIE, and ROCK-SPIRiT based on traditional uniform undersampling methods using in vivo human brain data evaluated by PSNR and mean structure similarity. Experimental results demonstrate that our proposed method (i.e. SMS with outline information or MVDS) improves image reconstruction performance by reducing noise and artifacts as compared to traditional methods. The joint use of outline information and MVDS further suppress the noise and artifacts in the reconstructed images,