Abstract
The pre-trained transformer demonstrates remarkable generalization ability in natural image processing. However, directly transferring it to magnetic resonance images faces two key challenges: the inability to adapt to the specificity of medical anatomical structures and the limitations brought about by the privacy and scarcity of medical data. To address these issues, this paper proposes a Self-Supervised Pretrained Transformer (SSPFormer) for MRI images, which effectively learns domain-specific feature representations of medical images by leveraging unlabeled raw imaging data. To tackle the domain gap and data scarcity, we introduce inverse frequency projection masking, which prioritizes the reconstruction of high-frequency anatomical regions to enforce structure-aware representation learning. Simultaneously, to enhance robustness against real-world MRI artifacts, we employ frequency-weighted FFT noise enhancement that injects physiologically realistic noise into the Fourier domain. Together, these strategies enable the model to learn domain-invariant and artifact-robust features directly from raw scans. Through extensive experiments on segmentation, super-resolution, and denoising tasks, the proposed SSPFormer achieves state-of-the-art performance, fully verifying its ability to capture fine-grained MRI image fidelity and adapt to clinical application requirements.
Abstract (translated)
预训练的变压器在自然图像处理中展示了卓越的泛化能力。然而，直接将其应用于磁共振成像（MRI）时会面临两个关键挑战：一是无法适应医学解剖结构的特定性；二是由于医疗数据隐私性和稀缺性的限制带来的问题。为了解决这些问题，本文提出了一种用于MRI图像的自我监督预训练变压器（SSPFormer），该模型能够通过利用未标记的原始影像数据有效学习医学图像特有的特征表示。 为了应对领域差异和数据稀缺的问题，我们引入了逆频率投影掩码技术，优先重建高频率解剖区域，以此强化结构感知型表征学习。同时，为提高对现实世界MRI伪影的鲁棒性，我们采用了频率加权FFT噪声增强技术，在傅立叶域中注入生理真实的噪声。 通过这些策略，模型能够直接从原始扫描数据中学习到领域不变性和抗伪影特征。在分割、超分辨率和去噪任务上的广泛实验表明，所提出的SSPFormer达到了最先进的性能水平，充分验证了其捕捉精细MRI图像保真度及适应临床应用需求的能力。
URL
https://arxiv.org/abs/2601.12747
