Dual-Domain Equivariant Generative Adversarial Network for Multimodal CT-PET Synthesis

arXiv:2606.13341v1 Announce Type: cross Abstract: 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 th

Source: arXiv cs.AI — read the full report at the original publisher.

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