Extraction of a plasma time-activity curve from dynamic brain PET images based on independent component analysis

• Published in: IEEE transactions on biomedical engineering Vol. 52; no. 2; pp. 201 - 210
• Main Authors: Naganawa, M., Kimura, Y., Ishii, K., Oda, K., Ishiwata, K., Matani, A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blood; Brain - blood supply; Brain - diagnostic imaging; Brain - metabolism; Brain Mapping - methods; Compartment model; Computer Simulation; Data mining; Fluorodeoxyglucose F18 - blood; Fluorodeoxyglucose F18 - pharmacokinetics; Humans; Image Interpretation, Computer-Assisted - methods; Image sampling; Independent component analysis; Kinetic theory; Metabolic Clearance Rate; Models, Neurological; Models, Statistical; plasma time-activity curve extraction; Plasmas; Positron emission tomography; Positron-Emission Tomography - methods; Principal Component Analysis; Radiopharmaceuticals - blood; Radiopharmaceuticals - pharmacokinetics; Studies; Volume measurement
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2004.840193

A compartment model has been used for kinetic analysis of dynamic positron emission tomography (PET) data [e.g., 2-deoxy-2-/sup 18/F-fluoro-D-glucose (FDG)]. The input function of the model [the plasma time-activity curve (pTAC)] was obtained by serial arterial blood sampling. It is of clinical interest to develop a method for PET studies that estimates the pTAC without needing serial arterial blood sampling. For this purpose, we propose a new method to extract the pTAC from the dynamic brain PET images using a modified independent component analysis [extraction of the pTAC using independent component analysis (EPICA). Source codes of EPICA are freely available at http://www5f.biglobe.ne.jp//spl ap/ukimura/Software/top.html]. EPICA performs the appropriate preprocessing and independent component analysis (ICA) using an objective function that takes the various properties of the pTAC into account. After validation of EPICA by computer simulation, EPICA was applied to human brain FDG-PET studies. The results imply that the EPICA-estimated pTAC was similar to the actual measured pTAC, and that the estimated blood volume image was highly correlated with the blood volume image measured using /sup 15/O-CO inhalation. These results demonstrated that EPICA is useful for extracting the pTAC from dynamic PET images without the necessity of serial arterial blood sampling.