AI Insight
This study introduces a Dual-Domain Equivariant Generative Adversarial Network (DDE-GAN) that synthesizes multimodal CT-PET medical images by learning from both spatial and frequency domains simultaneously. The network incorporates rotational equivariance to maintain geometric consistency, addressing limitations of traditional GAN approaches that operate only in spatial domain. Tested on the HECKTOR 2022 dataset, DDE-GAN demonstrated superior performance compared to baseline models in generating anatomically accurate CT-PET images.
Why it matters
This advancement could improve clinical workflows by enabling PET scan completion when data is incomplete and augmenting training datasets for medical AI systems. The method's ability to generate high-quality multimodal medical images may reduce the need for repeated scans, lowering radiation exposure and costs for patients while maintaining diagnostic accuracy.
Understand the Science
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 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.
Source: Dual-Domain Equivariant Generative Adversarial Network for Multimodal CT-PET Synthesis