title: 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 for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures.
authors.affiliation: College of Computer Science, Sichuan University, Chengdu, People's Republic of China. IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.forename: Pei
authors.initials: P
authors.lastname: Zhang
authors.forename: Le
authors.initials: L
authors.lastname: An
authors.forename: Guangkai
authors.initials: G
authors.lastname: Ma
authors.forename: Jiayin
authors.initials: J
authors.lastname: Kang
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
authors.forename: Xi
authors.initials: X
authors.lastname: Wu
authors.forename: Jiliu
authors.initials: J
authors.lastname: Zhou
authors.forename: David S
authors.initials: DS
authors.lastname: Lalush
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D011829:Radiation Dosage