AI RESEARCH
FourierPET: Deep Fourier-based Unrolled Network for Low-count PET Reconstruction
arXiv CS.CV
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ArXi:2601.11680v2 Announce Type: replace-cross Low-count positron emission tomography (PET) reconstruction is a challenging inverse problem due to severe degradations arising from Poisson noise, photon scarcity, and attenuation correction errors. Existing deep learning methods typically address these in the spatial domain with an undifferentiated optimization objective, making it difficult to disentangle overlapping artifacts and limiting correction effectiveness.