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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
  • Xi Wu
  • Jiliu Zhou
  • David S. Lalush
  • Weili Lin
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

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.

Keywords

Positron emission tomography (PET) Sparse representation Incremental refinement Multimodal MR images 

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© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Yan Wang
    • 1
    • 2
  • Pei Zhang
    • 2
  • Le An
    • 2
  • Guangkai Ma
    • 2
  • Jiayin Kang
    • 2
  • Xi Wu
    • 3
  • Jiliu Zhou
    • 1
  • David S. Lalush
    • 4
    • 5
  • Weili Lin
    • 6
  • Dinggang Shen
    • 2
  1. 1.College of Computer ScienceSichuan UniversityChengduChina
  2. 2.IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of Computer ScienceChengdu University of Information TechnologyChengduChina
  4. 4.Joint Department of Biomedical EngineeringUniversity of North Carolina at Chapel HillChapel HillUSA
  5. 5.North Carolina State UniversityRaleighUSA
  6. 6.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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