Anatomical MRI-guided deep learning-based low-count PET image recovery without the need for training data – a PET/MR study

The advent of simultaneous PET/MRI enables the possibility of using MRI to guide PET image reconstruction/recovery. Deep-learning approaches have been explored in low-count PET recovery, with current approaches focus on supervised learning, which requires a large amount of training data. A recently proposed unsupervised learning image-recovery approach does not require this but relies on the optimal stopping criterion. In this work, we developed an unsupervised learning-based PET image recovery approach using anatomical MRI as input and a novel stopping criterion. Our method achieved better image recovery in both global image similarity metrics and regional standard uptake value (SUV) accuracy.