Quantitative imaging of coronary blood flow

Abstract

Positron emission tomography (PET) is a nuclear medicine imaging modality based on the administration of a positron-emitting radiotracer, the imaging of the distribution and kinetics of the tracer, and the interpretation of the physiological events and their meaning with respect to health and disease. PET imaging was introduced in the 1970s and numerous advances in radiotracers and detection systems have enabled this modality to address a wide variety of clinical tasks, such as the detection of cancer, staging of Alzheimer's disease, and assessment of coronary artery disease (CAD). This review provides a description of the logic and the logistics of the processes required for PET imaging and a discussion of its use in guiding the treatment of CAD. Finally, we outline prospects and limitations of nanoparticles as agents for PET imaging.

Automatic in‐line quantitative myocardial perfusion mapping: Processing algorithm and implementation

“…In the category of compartmental models, the exponential, 6 constrained Fermi function (Fermi), 7 or BSpline-based model free deconvolution 8 have been applied to myocardial perfusion. More comprehensive distributed parameter (DP) models have been applied to the estimate of MBF in MRI [9][10][11][12][13] and in positron emission tomography (PET) [14][15][16] based on modeling of the underlying physiology.…”

“…In the category of compartmental models, the exponential [6], Fermi function [7], or BSpline based model free deconvolution [8] have been applied to myocardial perfusion. More comprehensive distributed parameter models have been applied to the estimate of myocardial blood flow in MRI [9][10][11][12][13] and in PET [14][15][16] based on modeling of the underlying physiology. As illustrated in prior publications [6,11,[17][18][19][20][21][22][23][24], the assumption behind the simpler compartmental models is that Gd delivery to the myocardial interstitial space from the vascular space is flow limited, at least at the low flow scenario.…”