Purpose: To investigate whether contrast-informed data augmentation and domain-adversarial training improve the adult-to-neonatal generalization of the E2E-VarNet. Methods: Three training regimes were investigated: (1) adult-only training with unaugmented adult data, (2) mixed training with paired unaugmented and neonatal-informed augmented adult data, and (3) mixed training with a domain-adversarial objective. Models were trained on retrospectively undersampled multi-coil adult T2-weighted brain MR data and evaluated on neonatal and adult test data at acceleration factors $R=4$ and $R=8$ using quantitative metrics and qualitative evaluation. Feature analyses assessed whether domain-adversarial training altered the latent representations of unaugmented adult, augmented adult, and neonatal test samples. Results: Mixed training (Mixed) and mixed domain-adversarial training (Mixed-DAT) outperformed unaugmented adult-only training (Unaug-Only) when evaluated on neonatal data. At R=4, Mixed-DAT achieved the best performance (SSIM = 0.924 +/- 0.027, PSNR = 33.98 +/- 1.15 dB). At R=8, Mixed-DAT performed best when measured using SSIM (0.848 +/- 0.031 vs. 0.766 +/- 0.037 for Unaug-Only and 0.814 +/- 0.035 for Mixed) and Mixed performed best when measured using PSNR (29.56 +/- 0.83 dB vs. 26.26 +/- 0.78 dB for Unaug-Only and 29.43 +/- 0.83 dB for Mixed-DAT). Qualitative assessment of t-SNE plots suggested that Mixed-DAT increased the overlap among the latent representations of the unaugmented adult, augmented adult, and neonatal test data. Conclusion: Contrast-informed augmentation and domain-adversarial training improved adult-to-neonatal generalization of deep learning-based MR reconstruction. These findings suggest that contrast-informed data augmentation combined with adversarial training may improve robustness to domain shift in undersampled neonatal MR reconstruction.

翻译：目的：探究对比信息增强数据扩充与领域对抗训练是否提升E2E-VarNet在成人到新生儿磁共振重建中的泛化能力。方法：研究三种训练策略：(1) 仅使用未增强成人数据的训练，(2) 结合配对未增强数据与新生儿信息增强成人数据的混合训练，(3) 引入领域对抗目标的混合训练。模型基于回顾性降采样的多线圈成人T2加权脑MR数据进行训练，并在加速因子 $R=4$ 和 $R=8$ 下通过定量指标与定性评估对新生儿及成人测试数据进行分析。特征分析旨在评估领域对抗训练是否改变未增强成人、增强成人及新生儿测试样本的潜在表征。结果：混合训练与混合领域对抗训练在新生儿数据评估中均优于仅使用未增强成人数据的训练。在 $R=4$ 时，混合领域对抗训练取得最佳性能（SSIM=0.924±0.027，PSNR=33.98±1.15 dB）。在 $R=8$ 时，混合领域对抗训练在SSIM指标上表现最优（0.848±0.031，对比未增强成人训练0.766±0.037与混合训练0.814±0.035），而混合训练在PSNR指标上最优（29.56±0.83 dB，对比未增强成人训练26.26±0.78 dB与混合领域对抗训练29.43±0.83 dB）。t-SNE图的定性分析表明，混合领域对抗训练增强了未增强成人、增强成人及新生儿测试数据潜在表征间的重叠。结论：对比信息增强与领域对抗训练显著提升了基于深度学习的MR重建从成人到新生儿的泛化性能。研究结果表明，结合对比信息增强与对抗训练能改善降采样新生儿MR重建中对领域漂移的鲁棒性。