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Myocardial blood flow quantification using Rubidium-82 PET

  • Sabine Susanna Koenders

Research output: ThesisPhD Thesis - Research external, graduation UT

1330 Downloads (Pure)

Abstract

Myocardial perfusion imaging (MPI) using positron emission tomography (PET) has a high diagnostic value in the detection of obstructive coronary artery disease and is growing in its use. The addition of myocardial blood flow (MBF) and myocardial flow reserve (MFR) measurements to the visual assessment of PET images is making its way into clinical routine. MBF and MFR provide valuable additional diagnostic and prognostic information about the extent and functional importance of possible stenosis to visual assessment of PET images.

The quantification of MBF is conducted with kinetic modeling of the image-derived time-activity curves (TACs) allowing derivation of MBF in units of mL/min per gram of tissue. MFR is defined as the ratio of MBF during maximal coronary vasodilatation to resting MBF. In other words, the MFR gives an indication of the degree of coronary vasodilator capacity and reflects the extent of which a stenosis actually impairs the supply of blood to the myocardium when the demand increases.

In the process of data acquisition, image reconstruction, post-processing and interpretation of quantitative myocardial PET, there are several pitfalls that can result in unreliable blood flow quantification. In order for MBF and MFR quantification to achieve its full clinical potential, the technical aspects of MBF and MFR quantification must be well understood and standardized so that reliable MBF and MFR values can be routinely produced.

The aim of this thesis was to study and optimize technical aspects to obtain reliable MBF and MFR values with Rubidium-82 (Rb-82) PET MPI. Furthermore, we studied the clinical value of MFR in clinical practice.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Slump, C.H., Supervisor
  • van Dijk, J.D., Co-Supervisor
  • van Dalen, Jorn A., Co-Supervisor, External person
Award date30 Sept 2022
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-5404-6
DOIs
Publication statusPublished - 30 Sept 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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