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MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising
arXiv – CS AI|Peiyuan Jing, Chun-Wun Cheng, Liutao Yang, Zhenxuan Zhang, Thiago V. Lima, Klaus Strobel, Antoine Leimgruber, Angelica Aviles-Rivero, Guang Yang, Javier A. Montoya-Zegarra||4 views
🤖AI Summary
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.
Key Takeaways
- →MAP-Diff uses clinically observed intermediate-dose PET scans as trajectory anchors to guide the diffusion denoising process.
- →The framework enables progressive restoration from ultra-low-dose input while maintaining dose-consistent intermediate states.
- →Testing on Siemens and United Imaging scanner datasets showed consistent improvements over CNN, Transformer, GAN, and diffusion baselines.
- →The method achieved 1.23 dB PSNR improvement and reduced noise while maintaining quantitative accuracy.
- →Cross-scanner generalization was demonstrated, indicating robustness across different PET imaging systems.
#medical-ai#diffusion-models#pet-imaging#denoising#3d-reconstruction#healthcare-ai#computer-vision#medical-imaging
Read Original →via arXiv – CS AI
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