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
The continuous advancements in generative AI and increasing computational power enable more sophisticated medical imaging synthesis techniques, pushing the boundaries of what is possible in diagnostic tools.
This development significantly enhances the accuracy and efficiency of medical diagnostics by synthesizing complex multimodal images, potentially leading to earlier and more precise disease detection.
The ability to generate high-fidelity multimodal medical images with geometric consistency improves diagnostic certainty and reduces the need for multiple invasive procedures, impacting radiology workflows.
- · Medical Imaging Industry
- · Hospitals and Diagnostic Centers
- · AI Healthcare Startups
- · Patients
- · Traditional diagnostic methods reliant on single modalities
Improved diagnostic consistency and reduced errors in medical imaging interpretation.
Accelerated development of AI-driven diagnostic platforms and personalized treatment plans.
Enhanced accessibility to advanced diagnostic capabilities, particularly in regions with limited specialized equipment, through synthetic generation.
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Read at arXiv cs.AI