Purpose: Deep learning-based MRI artifact correction methods often demonstrate poor generalization to clinical data. This limitation largely stems from the inability of deep learning models in reliably distinguishing motion artifacts from true anatomical structures, due to insufficient awareness of artifact characteristics. To address this challenge, we proposed PERCEPT-Net, a deep learning framework that enhances structure preserving and suppresses artifact through dedicated perceptual supervision.Method: PERCEPT-Net is built on a residual U-Net backbone and incorporates three auxiliary components. The first multi-scale recovery module is designed to preserve both global anatomical context and fine structural details, while the second dual attention mechanisms further improve performance by prioritizing clinically relevant features. At the core of the framework is the third Motion Perceptual Loss (MPL), an artifact-aware perceptual supervision strategy that learns generalized representations of MRI motion artifacts, enabling the model to effectively suppress them while maintaining anatomical fidelity. The model is trained on a hybrid dataset comprising both real and simulated paired volumes, and its performance is validated on a prospective test set using a combination of quantitative metrics and qualitative assessments by experienced radiologists.Result: PERCEPT-Net outperformed state-of-the-art methods on clinical data. Ablation studies identified the Motion Perceptual Loss as the primary contributor to this performance, yielding significant improvements in structural consistency and tissue contrast, as reflected by higher SSIM and PSNR values. These findings were further corroborated by radiologist evaluations, which demonstrated significantly higher diagnostic confidence in the corrected volumes.

翻译：目的：基于深度学习的MRI伪影校正方法通常对临床数据泛化能力较差。这一局限性主要源于深度学习模型因缺乏对伪影特征的充分感知，难以可靠区分运动伪影与真实解剖结构。为解决这一挑战，我们提出PERCEPT-Net——一种通过专门设计的感知监督来增强结构保持并抑制伪影的深度学习框架。方法：PERCEPT-Net以残差U-Net为基础架构，融入三个辅助组件。第一个多尺度恢复模块旨在同时保留全局解剖背景和精细结构细节；第二个双重注意力机制通过优先处理临床相关特征进一步提升性能。框架核心在于第三个组件——运动感知损失（MPL），这是一种具备伪影感知能力的感知监督策略，可学习MRI运动伪影的泛化表征，使模型在保持解剖保真度的同时有效抑制伪影。模型采用包含真实与模拟配对体数据的混合训练集，并通过定量指标与经验丰富的放射科医师定性评估相结合的方法在前瞻性测试集上验证性能。结果：PERCEPT-Net在临床数据上优于现有最优方法。消融研究表明，运动感知损失是性能提升的主要驱动因素，其在结构一致性和组织对比度方面取得显著改善（表现为更高的SSIM与PSNR值）。放射科医师评估进一步证实这些发现，显示校正后体数据具有显著更高的诊断置信度。