Unsupervised denoising is useful as it allows low-count PET image recovery without the need of paired training data(low-count/full-count). However, current unsupervised denoising models utilize Contrast-to-Noise Ratio as stopping criteria to optimize the image recovery process, which can be improved by considering structural information to maintain the integrity of gross anatomy. In this work, we proposed an MRI structural regularization loss function for low-count PET image recovery using an unsupervised learning model, which does not require paired training sets and demonstrated that the proposed method is superior in both qualitative and quantitative analyses for two radiotracers with very different physiological uptake.
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