Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning

ArXi:2604.16925v1 Announce Type: new Cross-dose denoising for low-dose positron emission tomography (LDPET) has been proposed to address the limited generalization of models trained at a single noise level. In practice, neural networks trained on a specific dose level often fail to generalize to other dose conditions due to variations in noise magnitude and statistical properties. Conventional "one-size-for-all" models attempt to handle this variability but tend to formulations implicitly optimize an expectation over heterogeneous noise distributions.