Statistical approaches in quantitative positron emission tomography

Richard M. Leahy, Jinyi Qi

Research output: Contribution to journalArticlepeer-review

161 Scopus citations

Abstract

Positron emission tomography is a medical imaging modality for producing 3D images of the spatial distribution of biochemical tracers within the human body. The images are reconstructed from data formed through detection of radiation resulting from the emission of positrons from radioisotopes tagged onto the tracer of interest. These measurements are approximate line integrals from which the image can be reconstructed using analytical inversion formulae. However these direct methods do not allow accurate modeling either of the detector system or of the inherent statistical fluctuations in the data. Here we review recent progress in developing statistical approaches to image estimation that can overcome these limitations. We describe the various components of the physical model and review different formulations of the inverse problem. The wide range of numerical procedures for solving these problems are then reviewed. Finally, we describe recent work aimed at quantifying the quality of the resulting images, both in terms of classical measures of estimator bias and variance, and also using measures that are of more direct clinical relevance.

Original languageEnglish (US)
Pages (from-to)147-165
Number of pages19
JournalStatistics and Computing
Volume10
Issue number2
StatePublished - 2000
Externally publishedYes

Keywords

  • Bayesian imaging
  • Computed tomography
  • Image reconstruction
  • Maximum likelihood estimation
  • Positron emission tomography

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics and Probability
  • Theoretical Computer Science

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