Contrast-Informed Augmentation and Domain-Adversarial Training for Adult-to-Neonatal MR Reconstruction Generalization

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Science & Research — Health & Medical Research, Artificial Intelligence & Machine Learning · Depth: Advanced, extended

Summary

A study investigated improving the generalization of deep learning-based MR reconstruction models from adult to neonatal data using contrast-informed data augmentation and domain-adversarial training (DAT). Researchers trained the End-to-End Variational Network (E2E-VarNet) on retrospectively undersampled multi-coil adult T2-weighted brain MR data (n=55 volumes) and evaluated it on neonatal T2-weighted brain MR data (n=30 volumes) at acceleration factors R=4 and R=8. Three regimes were compared: adult-only, mixed with augmented adult data, and mixed with DAT. Both mixed training approaches outperformed adult-only training on neonatal data. Specifically, Mixed-DAT achieved the best performance at R=4 (SSIM=0.924±0.027, PSNR=33.98±1.15 dB) and the best SSIM at R=8 (0.848±0.031). Feature analysis indicated that Mixed-DAT increased the overlap of latent representations between adult and neonatal data, suggesting reduced domain separability, though it showed a modest trade-off in adult-domain performance.

Key takeaway

For Machine Learning Engineers developing MR reconstruction models for diverse patient populations, especially neonates, you should integrate contrast-informed data augmentation and domain-adversarial training (DAT). This approach significantly improves model generalization to neonatal MR data, as demonstrated by higher SSIM and PSNR scores. Be mindful that DAT may introduce a slight trade-off in adult-domain performance and that high acceleration factors like R=8 might still yield reconstructions insufficient for confident clinical interpretation.

Key insights

Contrast-informed augmentation and domain-adversarial training enhance deep learning MR reconstruction generalization from adult to neonatal data.

Principles

• Domain shift in medical imaging can be mitigated by targeted data augmentation.
• Adversarial training can reduce feature-space separation between source and target domains.
• Quantitative metrics (SSIM, PSNR) may show divergent trends with domain adaptation.

Method

Train E2E-VarNet with paired unaugmented and contrast-informed augmented adult MR data, integrating a domain discriminator with a gradient reversal layer to maximize domain classification loss for domain-invariant feature learning.

In practice

• Apply contrast-informed augmentation to adult MR data to mimic neonatal characteristics.
• Implement a domain discriminator with a gradient reversal layer in DL-based MR reconstruction.
• Evaluate domain adaptation strategies using both SSIM and PSNR, noting potential trade-offs.

Topics

• MR Reconstruction
• Deep Learning Generalization
• Domain-Adversarial Training
• Contrast-Informed Augmentation
• Neonatal MRI
• E2E-VarNet

Code references

• moorestephen/unaug-aug-dat-mr-recon

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.