用螺旋扫描提升医学影像生成，兼顾全局结构与局部细节。
FermatSyn: SAM2-Enhanced Bidirectional Mamba with Isotropic Spiral Scanning for Multi-Modal Medical Image Synthesis
- 引入SAM2先验编码器，结合LoRA+微调实现解剖知识注入。
- 提出双向螺旋扫描Mamba，减少方向偏差并保持高频病变细节。
- 生成图像可直接用于分割任务，效果接近真实数据训练。
多模态医学图像合成对缓解临床数据稀缺至关重要，但现有方法难以同时保证全局解剖一致性与局部高保真细节。本文提出FermatSyn，解决三大问题：(1) 基于SAM2的先验编码器，通过LoRA+高效微调冻结的SAM2视觉变压器注入领域感知解剖知识；(2) 分层残差下采样模块（HRDM）与跨尺度融合网络（CIN），保留高频病灶细节并自适应融合全局-局部表征；(3) 在双向费马螺旋扫描Mamba（BFS-Mamba）中采用连续性约束的费马螺旋扫描策略，构建近似各向同性的感受野，显著降低栅格或螺旋序列化带来的方向偏差。在SynthRAD2023、BraTS2019、BraTS-MEN和BraTS-MET数据集上的实验表明，FermatSyn在PSNR、SSIM、FID及3D结构一致性上均超越当前最优方法。基于合成图像的下游分割任务性能与真实图像训练无显著差异（p>0.05），验证其临床可用性。代码已开源。
原文摘要 · Abstract (English)
Multi-modal medical image synthesis is pivotal for alleviating clinical data scarcity, yet existing methods fail to reconcile global anatomical consistency with high-fidelity local detail. We propose FermatSyn, which addresses three persistent limitations: (1) SAM2-based Prior Encoder that injects domain-aware anatomical knowledge via LoRA$^{+}$ efficient fine-tuning of a frozen SAM2 Vision Transformer; (2) Hierarchical Residual Downsampling Module (HRDM) coupled with a Cross-scale Integration Network (CIN) that preserves high-frequency lesion details and adaptively fuses global--local representations; and (3) continuity constrained Fermat Spiral Scanning strategy within a Bidirectional Fermat Scan Mamba (BFS-Mamba), constructing an approximately isotropic receptive field that substantially reduces the directional bias of raster or spiral serialization. Experiments on SynthRAD2023, BraTS2019, BraTS-MEN, and BraTS-MET show FermatSyn surpasses state-of-the-art methods in PSNR, SSIM, FID, and 3D structural consistency. Downstream segmentation on synthesized images yields no significant difference from real-image training ($p{>}0.05$), confirming clinical utility. Code is available at https://github.com/gatina-yone/FermatSyn.
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