Determination of arterial input function using fuzzy clustering for quantification of cerebral blood flow with dynamic susceptibility contrast-enhanced MR imaging
K. Murase et al., Determination of arterial input function using fuzzy clustering for quantification of cerebral blood flow with dynamic susceptibility contrast-enhanced MR imaging, J MAGN R I, 13(5), 2001, pp. 797-806
An accurate determination of the arterial input function (AIF) Is necessary
for quantification of cerebral blood flow (CBF) using dynamic susceptibili
ty contrast-enhanced magnetic resonance imaging. In this study, we develope
d a method for obtaining the AIF automatically using fuzzy c-means (FCM) cl
ustering. The validity of this approach was investigated with computer simu
lations. We found that this method can automatically extract the AIF, even
under very noisy conditions, e.g., when the signal-to-noise ratio is 2. The
simulation results also Indicated that when using a manual drawing of a re
gion of interest (ROI) (manual ROI method), the contamination of surroundin
g pixels (background) into ROI caused considerable overestimation of CBF. W
e applied this method to six subjects and compared it with the manual ROI m
ethod. The CBF values, calculated using the AIF obtained using the manual R
OI method [CBF(manual)], were significantly higher than those obtained with
FCM clustering [CBF(fuzzy)]. This may have been due to the contamination o
f non-arterial pixels into the manually drawn ROI, as suggested by simulati
on results. The ratio of CBF(manual) to CBF(fuzzy) ranged from 0.99-1.83 [1
.31 +/- 0.26 (mean +/- SD)]. In conclusion, our FCM clustering method appea
rs promising for determination of AIF because it allows automatic, rapid an
d accurate extraction of arterial pixels. (C) 2001 Wiley-Liss, Inc.