SSPFormer: Self-Supervised Pretrained Transformer for MRI Images

TL;DR

SSPFormer is a self-supervised transformer model trained on unlabeled MRI data, using novel masking and noise strategies to learn robust, domain-specific features for improved medical image analysis tasks.

Contribution

This work introduces SSPFormer, a self-supervised pretrained transformer that effectively learns domain-specific features from unlabeled MRI data using innovative masking and noise techniques.

Findings

Achieves state-of-the-art results in segmentation, super-resolution, and denoising.

Learns domain-invariant and artifact-robust features from raw MRI scans.

Demonstrates strong generalization and clinical applicability.

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.