arXiv:2602.22545 · v4

SFL-Net: Dependence-Regularized Latent Pathways for Multi-Contrast MRI-to-tau-PET Synthesis and Component Attribution

首次提交 2026-02-26 10:37

版本更新 2026-11-12 07:57

雷达观测 2026-11-13 14:16

摘要

Structural MRI-to-tau-PET synthesis estimates molecular imaging patterns from indirect anatomical correlates. We present SFL-Net, a vector-quantized architecture with shared, T1-specific, FLAIR-specific, and joint-input latent pathways. Dependence regularization differentiates these pathways, while latent-derived structural conditioning supports decoding without direct encoder-decoder feature skips. We evaluated SFL-Net and capacity-matched VAE, VQ-VAE, U-Net, and SPADE-U-Net variants using 605 training and 83 internal validation subjects pooled from ADNI-3 and OASIS-3. SFL-Net achieved the lowest raw PET MAE and MSE and the highest PSNR among the implemented models, while SPADE-U-Net variants performed better on several SUVR and staging metrics. Exact coalitional Shapley attribution assigned the largest average marginal contribution to the joint-input pathway, with substantial variation across subjects. These contributions describe decoder dependence under latent replacement and retained structural conditioning; they do not establish identifiable information-theoretic synergy or the incremental benefit of combining MRI contrasts. Predictions showed dynamic-range compression at higher tau burden, and several staging metrics were close to a constant Stage 1 reference. SFL-Net provides a framework for structured latent-component interrogation in multi-input image synthesis, linking architectural pathways to reconstruction behavior.