Digging Deeper in Gradient for Unrolling-Based Accelerated MRI Reconstruction

There are two main methods that can be used to accelerate MRI reconstruction: parallel imaging and compressed sensing. To further accelerate the sampling process, the combination of these two methods has been extensively studied in recent years. However, existing MRI reconstruction methods often overlook the exploration of high-frequency information of images, leading to sub-optimal recovery of fine details in the reconstructed results. To address this issue, we conduct an in-depth analysis of image gradients and propose a novel MRI reconstruction model based on Maximum a Posteriori (MAP) estimation. We first establish the Cumulative Deviation from Maximum Gradient magnitude (CDMG) prior for fully sampled MR images through theoretical analysis, then incorporate this explicit CDMG prior along with an implicit deep prior to form the prior probability term. This combination of priors strikes a balance between physically informed constraints and data-driven adaptability, aiding in the recovery of meaningful high-frequency information. Additionally, we introduce a multi-order gradient operator to enhance the observation model, thereby improving the accuracy of the likelihood term. Through MAP estimation, we develop a novel accelerated MRI reconstruction model, the optimization of which is achieved by unrolling it into a convolutional neural network structure, referred to as DDGU-Net. Extensive experimental results demonstrate the effectiveness of our approach in reconstructing high-quality MR images and achieving state-of-the-art (SOTA) results, particularly at higher acceleration factors.