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

Yan Wang, Pei Zhang, Le An, Guangkai Ma, Jiayin Kang, Feng Shi, Xi Wu, Jiliu Zhou, David S. Lalush, Weili Lin, Dinggang Shen

Research output: Contribution to journalArticle

20 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)791-812
Number of pages22
JournalPhysics in Medicine and Biology
Volume61
Issue number2
DOIs
Publication statusPublished - 2016 Jan 6

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Keywords

  • incremental refinement
  • mapping-based sparse representation
  • multimodal MR images
  • positron emission tomography (PET)
  • sparse representation
  • standard-dose PET prediction

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

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