Magnetic resonance imaging (MRI) is an imaging technique that provides comprehensive anatomical and functional information on the human body, but prolonged acquisition of fully sampled MRI images causes patient discomfort and motion artifacts. In recent years, deep learning (DL) has made significant progress in accelerating MRI image reconstruction. At present, MRI image reconstruction is concentrated on single domain, but hybrid domain reconstruction has more advantages. Here, we propose a hybrid domain reconstruction method based on AUTOMAP—KDI-Net, which divided into three parts: K-Block, D-Block and I-Block. We evaluated the performance of our KDI-Net model for MRI image reconstruction using PSNR, RMSE, and SSIM metrics and three reduction factors (2, 3, 4) on two publicly available MRI datasets (the AUTOMAP brain dataset and the Calgary Campinas single-coil brain dataset). The results show that KDI-Net performs better than AUTOMAP and Complex AUTOMAP. Compared to AUTOMAP, KDI-Net basically improved each metric by more than 6%. Compared to Complex AUTOMAP, KDI-Net basically improved by more than 8% on each metric. For reduction factor 2, the reconstructed MRI image is very close to ground true. Our specially designed KDI-Net can extract the sparsity from single channel MRI K-space, which achieve 2 folder acceleration.
Accurate segmentation of the hippocampus from MR brain images is a critical step for investigating early brain development. Unfortunately, the previous methods hippocampus segmentation is not suitable for subdividing hippocampus into multiple anatomically different subregions, such as head, body, tail, based on structural magnetic resonance imaging. The main problem is lack of discriminative and robust features representation for distinguishing the hippocampus from the surrounding subregions structures. To this end, we designed a dual-pathway network to learn the global and local representation from both global and local pathway. We proposed a global pyramid attention fusion module (GPAFM) in global pathway, which extracted coarse and global features representing the gist of the object. And a local feature aggregation module was adopted in local pathway to extract fine and local features representing the details for determining object categories. The experimental results have demonstrated that GPAFM successfully parcellated the hippocampus into head, body and tail subregions with high accuracy. Dice coefficient on three hippocampus datasets have further demonstrated the validity of the model. Compared with superior methods, the proposed method had better performance on parcellating the hippocampus subregions.
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