A factor-image framework to quantification of brain receptor dynamic PET studies

• Published in: IEEE transactions on signal processing Vol. 53; no. 9; pp. 3473 - 3487
• Main Authors: Wang, Z.J., Szabo, Z., Peng Lei, Varga, J., Liu, K.J. Ray
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2005; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Binding; Blood; Brain; Brain receptor study; compartmental model; distribution volume; dynamic imaging; Exact sciences and technology; Heterogeneity; Image processing; Image sampling; Information, signal and communications theory; Kinetic theory; likelihood; Mathematical models; Medical imaging; Neurotransmitters; Parameter estimation; PET; Pixel; Positron emission; Positron emission tomography; Receptors; Sampling; Sampling methods; Signal processing; Spatial resolution; Telecommunications and information theory; Time measurement; tracer kinetic modeling; voxel-domain quantitative imaging; Performance evaluation; Scanner; compartmental model; distribution volume; Image sampling; Subspace method; Iterative method; Algorithm; Modeling; dynamic imaging; Heterogeneity; Positron computed tomography; voxel-domain quantitative imaging; Simulation; Brain receptor study; likelihood; Imaging; Voxel; Tomography; Measurement method; Spatial resolution; PET; tracer kinetic modeling
• ISSN: 1053-587X; 1941-0476; 1941-0476
• DOI: 10.1109/TSP.2005.853149

The positron emission tomography (PET) imaging technique enables the measurement of receptor distribution or neurotransmitter release in the living brain and the changes of the distribution with time and thus allows quantification of binding sites as well as the affinity of a radioligand. However, quantification of receptor binding studies obtained with PET is complicated by tissue heterogeneity in the sampling image elements (i.e., voxels, pixels). This effect is caused by a limited spatial resolution of the PET scanner. Spatial heterogeneity is often essential in understanding the underlying receptor binding process. Tracer kinetic modeling also often requires an intrusive collection of arterial blood samples. In this paper, we propose a likelihood-based framework in the voxel domain for quantitative imaging with or without the blood sampling of the input function. Radioligand kinetic parameters are estimated together with the input function. The parameters are initialized by a subspace-based algorithm and further refined by an iterative likelihood-based estimation procedure. The performance of the proposed scheme is examined by simulations. The results show that the proposed scheme provides reliable estimation of factor time-activity curves (TACs) and the underlying parametric images. A good match is noted between the result of the proposed approach and that of the Logan plot. Real brain PET data are also examined, and good performance is observed in determining the TACs and the underlying factor images.