Matsuda et al. have described a non-invasive method for brain perfusion qua
ntification by computing the ratio of the cumulated counts in the cerebral
hemispheres and aortic arch. The regions of interest (ROIs) are drawn manua
lly and are observer dependent. The aim of this study was to develop a new
method designed to minimize the intra- and interobserver variability when d
rawing the different ROIs. A dynamic study was performed as in Matsuda's me
thod on 30 patients using technetium-99m ethyl cysteinate dimer (Tc-99(m)-E
CD) (ID: 800 MBq+/-33 MBq). The manual method of drawing ROIs was then comp
ared with the following, automated one. A temporal analysis was performed o
n the cardiac first-pass study to obtain parametric images of the thorax. A
n ROI of the aortic arch was drawn automatically by means of an isocontour
algorithm on the resulting views. The whole sequence was reframed and filte
red by a temporal low-pass filter. Hemispheric brain ROIs were delineated o
n a summed image. Matsuda's algorithm was then applied. Intraobserver varia
bility was evaluated for the classical Matsuda method. The correlation in b
rain perfusion index (BPI) measurements was r = 0.8976 for naive observers
and r = 0.9443 for well-trained observers. Interobserver variability was al
so evaluated; the correlation was r = 0.7574 for naive observers and r = 0.
9190 for well-trained observers. With our proposed method, the correlation
in the measurements of BPI for evaluating the intraobserver variability was
r = 0.9955 for naive observers and r = 0.9989 for well-trained observers.
For interobserver variability, the results were r=0.9234 for naive observer
s and r = 0.9230 for well-trained observers. We conclude that temporal anal
ysis allows brain perfusion to be measured in a semi-automatic manner, and
improves the reproducibility compared with the original method of Matsuda,
particularly for naive observers. ((C) 2000 Lippincott Williams & Wilkins).