Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

Abstract

Positron emission tomography (PET) has been widely used in clinical diagnosis of diseases or disorders. To reduce the risk of radiation exposure, we propose a mapping-based sparse representation (m-SR) framework for prediction of standard-dose PET image from its low-dose counterpart and corresponding multimodal magnetic resonance (MR) images. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients estimated from the low-dose PET and multimodal MR images could be directly applied to the prediction of standard-dose PET images. An incremental refinement framework is also proposed to further improve the performance. Finally, a patch selection based dictionary construction method is used to speed up the prediction process. The proposed method has been validated on a real human brain dataset, showing that our method can work much better than the state-of-the-art method both qualitatively and quantitatively.