A1 Refereed original research article in a scientific journal

A NEW GRAPHIC PLOT ANALYSIS FOR CEREBRAL BLOOD-FLOW AND PARTITION-COEFFICIENT WITH IODINE-123-IODOAMPHETAMINE AND DYNAMIC SPECT VALIDATION STUDIES USING OXYGEN-15-WATER AND PET




AuthorsYOKOI T, IIDA H, ITOH H, KANNO I

PublisherSOC NUCLEAR MEDICINE INC

Publication year1993

JournalJournal of Nuclear Medicine

Journal name in sourceJOURNAL OF NUCLEAR MEDICINE

Journal acronymJ NUCL MED

Volume34

Issue3

First page 498

Last page505

Number of pages8

ISSN0161-5505


Abstract
To estimate regional cerebral blood flow (rCBF) and brain-blood partition coefficient (lambda) using a dynamic measurement, a new graphic plot analysis is proposed. By assuming a two-compartment model for tracer kinetics, we derived the linear relationship as Y(t) = K1 - k2 X(t), where Y(t) is the ratio of brain tissue activity-to-time-integrated arterial blood activity and X(t) is the ratio of time-integrated brain tissue activity-to-time-integrated arterial blood activity. A plot of Y(t) against X(t) yields a straight line and the y- and x-intercept of the regression line represent rCBF (K1) and lambda, respectively. The slope is a washout constant (-k2). This method was applied to 14 subjects with N-isopropyl-p-iodine-1 23 iodoamphetamine ([I-123]IMP). The mean values of K1 and lambda for normal subjects were 41.3 +/- 6.7 ml/100 g/min and 29.6 +/- 6.5 ml/g, respectively, in the gray matter. A comparative study with positron emission tomography (PET) using an (H2O)-O-15 autoradiographic method revealed good correlation between IMP K1 and PET rCBF [r = 0.822; K1 = 0.842 rCBF + 0.030 (ml/g/min)]. The values of K1 using the graphical method were in excellent agreement with those using a nonlinear least-squares fitting technique (r = 0.992 for K1; r = 0.941 for lambda). The estimated K1 values in the graphical method were not changed when scanning times were varied. We conclude that a two-compartment model is acceptable for IMP kinetics within a scan time of 60 min. The graphical method gives a reliable and rapid estimation of rCBF when applied to dynamic data.



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