Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
Predicting Standard-Dose PET Image from Low-Dose PET and Multimodal MR Images Using Mapping-Based Sparse Representation
verfasst von : Yan Wang, Pei Zhang, Le An, Guangkai Ma, Jiayin Kang, Xi Wu, Jiliu Zhou, David S. Lalush, Weili Lin, Dinggang Shen
Erschienen in: Machine Learning in Medical Imaging
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.