3 articles tagged with #pet-imaging. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed WCC-Net, a 3D wavelet-based diffusion model that significantly improves low-dose PET imaging denoising while reducing patient radiation exposure. The AI framework uses frequency-domain structural priors to maintain anatomical accuracy and outperforms existing CNN, GAN, and diffusion baselines across multiple dose levels.
Researchers introduce Delta-Diffusion, a novel AI framework using conditional Poisson Diffusion Bridges to synthesize longitudinal brain PET imaging for tracking amyloid accumulation in neurodegenerative diseases. The method addresses limitations of existing generative models by anchoring predictions to baseline patient scans and incorporating clinical progression patterns, potentially reducing the need for costly repeated imaging procedures.
Researchers developed MAP-Diff, a multi-anchor guided diffusion framework that improves 3D whole-body PET scan denoising by using intermediate-dose scans as trajectory anchors. The method achieves significant improvements in image quality metrics, increasing PSNR from 42.48 dB to 43.71 dB while reducing radiation exposure for patients.
