PathoField: Capacity-Controlled Feature-Space Residual Adaptation for Few-Shot Healthy-to-Pathological 3T-to-7T MRI Synthesis

Paired pathological 3T–7T MRI is scarce, making adaptation of high-capacity synthesis models difficult in the few-shot regime. We introduce PathoField, a capacity-controlled transfer strategy that freezes a healthy-pretrained 3T-to-7T generator and learns four residual bottleneck adapters in decoder feature space. PathoField exposes only 45,696 trainable parameters (0.35% of the 13.20M-parameter backbone), approximately $290\times$ fewer than unrestricted full fine-tuning, while preserving the pretrained mapping exactly at initialization. We further characterize the asymmetric optimization induced by zero initialization and derive a local capacity-dependent generalization analysis. On paired Penn epilepsy data, all reported test checkpoints are re-evaluated with a common all-slice spatial-SSIM/PSNR protocol. At $k=16$, PathoField reaches 0.7941 SSIM / 21.15,dB with 45,696 trainable parameters, compared with 0.8015 / 21.12,dB for Full-FT with 13.25M parameters and 0.7117 / 21.10,dB for rank-3 Conv-LoRA with 54,912 parameters. Thus, on this fixed split, PathoField comes within 0.0074 SSIM of unrestricted fine-tuning while improving SSIM by 0.0824 over the comparable-budget weight-space baseline. A separate 23-subject frozen-transfer audit yields 0.4731 SSIM / 15.11,dB. These results reveal a data-dependent fidelity–capacity trade-off: strong capacity restriction incurs a clear cost in the lowest-data regimes, but at $k=16$, PathoField recovers most of Full-FT structural fidelity with approximately $290\times$ fewer trainable parameters.