通过双域等变机制，提升多模态CT-PET图像合成的结构准确性和鲁棒性。
Dual-Domain Equivariant Generative Adversarial Network for Multimodal CT-PET Synthesis
- 联合空间与频域学习，融合解剖与光谱信息
- 引入旋转等变性损失，提升图像在旋转下的一致性
- 适合医学影像合成、数据增强与PET补全任务
我们提出一种双域等变生成对抗网络（DDE-GAN）用于多模态CT-PET图像合成。传统GAN仅在空间域操作，忽略几何一致性，导致结构保真度有限。DDE-GAN通过联合学习空间域与频域（傅里叶域），捕捉互补的解剖与光谱信息。同时，将CT与PET测量中的旋转等变性特性融入生成器与判别器的损失函数，确保旋转下响应一致，提升解剖准确性。采用分层双域训练策略，通过多阶段损失函数强制域内与域间一致性。在HECKTOR 2022 CT-PET数据集上评估，DDE-GAN在图像合成质量上优于基线模型。结果表明，结合双域学习与几何等变性可显著提升多模态图像合成的准确性和鲁棒性，适用于PET补全与数据增强等实际应用。
原文摘要 · Abstract (English)
We present a Dual-Domain Equivariant Generative Adversarial Network (DDE-GAN) for multimodal CT-PET image synthesis. Traditional GAN-based approaches often operate solely in the spatial domain and ignore geometric consistency, resulting in limited structural fidelity. DDE-GAN addresses these challenges by jointly learning from both spatial and frequency (Fourier) domains, capturing complementary anatomical and spectral information. Furthermore, rotational equivariance embedded in the physics of the CT and PET measurements are integrated into the loss of both the generator and discriminator to ensure consistent responses under rotations, improving anatomical accuracy. A hierarchical dual-domain training strategy enforces intra- and inter-domain consistency through multi-stage loss functions. Evaluated on the HECKTOR 2022 CT-PET dataset, DDE-GAN achieves superior synthesis quality over baseline models for CT-PET image synthesis. The results demonstrate that combining dual-domain learning with geometric equivariance substantially enhances multimodal image synthesis accuracy and robustness, enabling practical applications in PET completion and data augmentation.
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