PET image reconstruction using kernel method

Guobao Wang, Jinyi Qi

Research output: Contribution to journalArticle

65 Scopus citations

Abstract

Image reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in 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 the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4-D dynamic PET patient dataset showed promising results.

Original languageEnglish (US)
Article number6868314
Pages (from-to)61-71
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume34
Issue number1
DOIs
StatePublished - Jan 1 2015

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Keywords

  • Expectation maximization (EM)
  • image prior
  • image reconstruction
  • kernel method
  • positron emission tomography (PET)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software

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