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The long acquisition time has limited the accessibility of magnetic resonanceimaging (MRI) because it leads to patient discomfort and motion artifacts.Although several MRI techniques have been proposed to reduce the acquisitiontime, compressed sensing in magnetic resonance imaging (CS-MRI) enables fastacquisition without compromising SNR and resolution. However, existing CS-MRImethods suffer from the challenge of aliasing artifacts. This challenge resultsin the noise-like textures and missing the fine details, thus leading tounsatisfactory reconstruction performance. To tackle this challenge, we proposea hierarchical perception adversarial learning framework (HP-ALF). HP-ALF canperceive the image information in the hierarchical mechanism: image-levelperception and patch-level perception. The former can reduce the visualperception difference in the entire image, and thus achieve aliasing artifactremoval. The latter can reduce this difference in the regions of the image, andthus recover fine details. Specifically, HP-ALF achieves the hierarchicalmechanism by utilizing multilevel perspective discrimination. Thisdiscrimination can provide the information from two perspectives (overall andregional) for adversarial learning. It also utilizes a global and localcoherent discriminator to provide structure information to the generator duringtraining. In addition, HP-ALF contains a context-aware learning block toeffectively exploit the slice information between individual images for betterreconstruction performance. The experiments validated on three datasetsdemonstrate the effectiveness of HP-ALF and its superiority to the comparativemethods.

Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI