PET image reconstruction using kernel method

Guobao Wang, Jinyi Qi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Inspired by the kernel methods for machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by maximum likelihood or penalized likelihood image reconstruction. Computer simulation shows that the proposed approach can achieve a higher signal-to-noise ratio for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising.

Original languageEnglish (US)
Title of host publicationProceedings - International Symposium on Biomedical Imaging
Pages1162-1165
Number of pages4
DOIs
StatePublished - 2013
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: Apr 7 2013Apr 11 2013

Other

Other2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
CountryUnited States
CitySan Francisco, CA
Period4/7/134/11/13

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Wang, G., & Qi, J. (2013). PET image reconstruction using kernel method. In Proceedings - International Symposium on Biomedical Imaging (pp. 1162-1165). [6556686] https://doi.org/10.1109/ISBI.2013.6556686