Skip to main content

Predicting Standard-Dose PET Image from Low-Dose PET and Multimodal MR Images Using Mapping-Based Sparse Representation

  • Conference paper
  • First Online:
Machine Learning in Medical Imaging (MLMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9352))

Included in the following conference series:


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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Chen, W.: Clinical applications of PET in brain tumors. Journal of Nuclear Medicine 48(9), 1468–1481 (2007)

    Article  Google Scholar 

  2. Quigley, H., Colloby, S.J., O’Brien, J.T.: PET imaging of brain amyloid in dementia: a review. International Journal of Geriatric Psychiatry 26(10), 991–999 (2011)

    Article  Google Scholar 

  3. Bai, W., Brady, M.: Motion correction and attenuation correction for respiratory gated PET images. IEEE Transactions on Medical Imaging 30, 351–365 (2011)

    Article  Google Scholar 

  4. Gigengack, F., Ruthotto, L., Burger, M., Wolters, C.H., Jiang, X., Schafers, K.P.: Motion correction in dual gated cardiac PET using mass-preserving image registration. IEEE Transactions on Medical Imaging 31(3), 698–712 (2012)

    Article  Google Scholar 

  5. Liu, Y., Ghesani, N.V., Zuckier, L.S.: Physiology and pathophysiology of incidental findings detected on FDG-PET scintigraphy. Seminars in Nuclear Medicine 40(4), 294–315 (2010)

    Article  Google Scholar 

  6. Lumbreras, B., Donat, L., Hernandez-Aguado, I.: Incidental findings in imaging diagnostic tests: a systematic review. The British Journal of Radiology 83(988), 276–289 (2014)

    Article  Google Scholar 

  7. Boss, A., Bisdas, S., Kolb, A., Hofmann, M., Ernemann, U., Claussen, C.D., Stegger, L.: Hybrid PET/MRI of intracranial masses: initial experiences and comparison to PET/CT. Journal of Nuclear Medicine 51(8), 1198–1205 (2010)

    Article  Google Scholar 

  8. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Transactions on Image Processing 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  9. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67(2), 301–320 (2005)

    Article  MathSciNet  Google Scholar 

  10. Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., Johansen-Berg, H., Matthews, P.M.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(S1), S208–S219 (2004)

    Article  Google Scholar 

  11. Tournier, J.D., Mori, S., Leemans, A.: Diffusion tensor imaging and beyond. Magnetic Resonance in Medicine 65(6), 1532–1556 (2011)

    Article  Google Scholar 

  12. Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE Transactions on Medical Imaging 13(4), 601–609 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Dinggang Shen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Wang, Y. et al. (2015). Predicting Standard-Dose PET Image from Low-Dose PET and Multimodal MR Images Using Mapping-Based Sparse Representation. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24887-5

  • Online ISBN: 978-3-319-24888-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics