UniPET proposes a universal PET denoising network with style alignment network (SAN) and region-aware learning strategy (RALS) to handle varied dose reduction factors via domain generalization.
arXiv preprint arXiv:2103.10541
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HDDPM adapts DDPMs with a fixed Poisson-based heteroscedastic forward process and dose conditioning to improve quantitative recovery of low-count brain PET images across scanners.
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UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors
UniPET proposes a universal PET denoising network with style alignment network (SAN) and region-aware learning strategy (RALS) to handle varied dose reduction factors via domain generalization.
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HDDPM: Heteroscedastic Denoising Diffusion Probabilistic Model for Quantitative Low-Count Brain PET Recovery
HDDPM adapts DDPMs with a fixed Poisson-based heteroscedastic forward process and dose conditioning to improve quantitative recovery of low-count brain PET images across scanners.