{"slug": "motion-artifact-aware-self-supervised-representation-learning-for-3d-brain-mri", "title": "Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reduction", "summary": "Researchers proposed SSRL-MAR, a motion artifact-aware self-supervised representation learning framework for 3D brain MRI motion artifact reduction that requires no paired training data or motion labels. On the in-silico dataset, SSRL-MAR achieved PSNR 23.81 dB, SSIM 91.55%, and NMSE 0.79%; on the in-vivo MR-ART dataset, it improved PSNR by up to 2.0 dB after unsupervised domain adaptation and reduced volumetric error in structures like the corpus callosum by more than 50%.", "body_md": "arXiv:2608.10170v1 Announce Type: new\nAbstract: Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep learning methods for motion correction typically rely on paired clean-corrupted data or k-space acquisitions, which are rarely available in clinical settings. We propose SSRL-MAR, a motion artifact-aware unpaired representation learning framework for motion artifact reduction that requires neither paired training data nor explicit motion labels. SSRL-MAR employed a three-stage training strategy: (1) contrastive learning on 3D patches to extract motion representations by contrasting clean and synthetically corrupted images, (2) a motion artifact-aware synthesis network to generate motion artifacts from clean scans, and (3) a motion artifact-aware generator to restore clean volumes using the learned degrader for self-supervised supervision. On in-silico dataset, SSRL-MAR achieved PSNR 23.81dB, SSIM 91.55%, and NMSE 0.79%. On in-vivo MR-ART dataset, the pretrained model reduced motion distortion, and unsupervised domain adaptation further improved anatomical fidelity. Against a source-only supervised model trained on the same simulated pairs, SSRL-MAR improved PSNR by up to 2.0 dB on MR-ART after unsupervised domain adaptation, and remained within 0.25-0.47 dB of an oracle supervised model that requires real paired data unavailable in practice. At the milder motion level, volumetric error in structures such as the corpus callosum and ventricular system decreased by more than 50%, confirming improved neuroanatomical consistency. These results indicate that SSRL-MAR provides a robust and scalable image-domain solution for 3D brain MRI motion correction, enabling reliable structural quantification in large-scale neuroimaging studies without requiring prospectively acquired pairs or acquisition-specific calibration.", "url": "https://wpnews.pro/news/motion-artifact-aware-self-supervised-representation-learning-for-3d-brain-mri", "canonical_source": "https://arxiv.org/abs/2608.10170", "published_at": "2026-08-12 04:00:00+00:00", "updated_at": "2026-08-12 04:11:05.086309+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "artificial-intelligence"], "entities": ["SSRL-MAR", "MR-ART"], "alternates": {"html": "https://wpnews.pro/news/motion-artifact-aware-self-supervised-representation-learning-for-3d-brain-mri", "markdown": "https://wpnews.pro/news/motion-artifact-aware-self-supervised-representation-learning-for-3d-brain-mri.md", "text": "https://wpnews.pro/news/motion-artifact-aware-self-supervised-representation-learning-for-3d-brain-mri.txt", "jsonld": "https://wpnews.pro/news/motion-artifact-aware-self-supervised-representation-learning-for-3d-brain-mri.jsonld"}}