Contrast-Informed Augmentation and Domain-Adversarial Training Improve MRI Reconstruction for Neonatal Imaging
Researchers developed a method combining contrast-informed data augmentation and domain-adversarial training to improve how adult-trained MRI reconstruction models generalize to neonatal brain imaging. The approach outperformed standard adult-only training across multiple acceleration factors (R=4 and R=8), with mixed domain-adversarial training achieving the best structural similarity scores on neonatal data. This work addresses a practical clinical challenge: neonatal MRI scans have different tissue contrast than adult scans, making models trained on adult data less effective without adaptation.
The study investigated whether contrast-informed data augmentation and domain-adversarial training could improve the E2E-VarNet deep learning model's ability to reconstruct undersampled MRI data from neonates when trained primarily on adult data. Three training approaches were compared: adult-only training with unaugmented data, mixed training combining unaugmented and neonatal-informed augmented adult data, and mixed training with a domain-adversarial objective. Models were evaluated on retrospectively undersampled multi-coil T2-weighted brain MR data at two acceleration factors. Results showed that both mixed training approaches outperformed adult-only training on neonatal test data, with mixed domain-adversarial training achieving the highest structural similarity (SSIM = 0.924 at R=4). Feature analysis using t-SNE plots indicated that domain-adversarial training increased overlap in latent representations across adult, augmented adult, and neonatal samples, suggesting the model learned more domain-invariant features. The findings suggest that combining contrast-informed augmentation with adversarial training may enhance robustness to domain shift in undersampled neonatal MR reconstruction.
What's missing
The study does not discuss clinical validation on prospectively acquired neonatal data, potential computational costs of the domain-adversarial approach, or how the method might generalize to other MRI contrasts (T1-weighted, FLAIR) or anatomical regions beyond brain imaging.
What different sources said
- arXiv cs.AICenter
Contrast-Informed Augmentation and Domain-Adversarial Training for Adult-to-Neonatal MR Reconstruction Generalization
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