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Titolo:
Determination of arterial input function using fuzzy clustering for quantification of cerebral blood flow with dynamic susceptibility contrast-enhanced MR imaging
Autore:
Murase, K; Kikuchi, K; Miki, H; Shimizu, T; Ikezoe, J;
Indirizzi:
Osaka Univ, Sch Med, Dept Med Engn, Div Allied Hlth Sci, Suita, Osaka 5650871, Japan Osaka Univ Suita Osaka Japan 5650871 lth Sci, Suita, Osaka 5650871, Japan
Titolo Testata:
JOURNAL OF MAGNETIC RESONANCE IMAGING
fascicolo: 5, volume: 13, anno: 2001,
pagine: 797 - 806
SICI:
1053-1807(200105)13:5<797:DOAIFU>2.0.ZU;2-2
Fonte:
ISI
Lingua:
ENG
Soggetto:
HIGH-RESOLUTION MEASUREMENT; TRACER BOLUS PASSAGES; PERFUSION; VOLUME; IMAGES;
Keywords:
fuzzy c-means clustering; dynamic susceptibility contrast-enhanced MR imaging; arterial input function; cerebral blood flow; quantification;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Citazioni:
18
Recensione:
Indirizzi per estratti:
Indirizzo: Murase, K Osaka Univ, Sch Med, Dept Med Engn, Div Allied Hlth Sci, 1-7 Yamadaoka, Suita, Osaka 5650871, Japan Osaka Univ 1-7 Yamadaoka Suita Osaka Japan 5650871 650871, Japan
Citazione:
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

Abstract

An accurate determination of the arterial input function (AIF) Is necessary for quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast-enhanced magnetic resonance imaging. In this study, we developed a method for obtaining the AIF automatically using fuzzy c-means (FCM) clustering. The validity of this approach was investigated with computer simulations. We found that this method can automatically extract the AIF, evenunder 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 region of interest (ROI) (manual ROI method), the contamination of surrounding pixels (background) into ROI caused considerable overestimation of CBF. We applied this method to six subjects and compared it with the manual ROI method. The CBF values, calculated using the AIF obtained using the manual ROI method [CBF(manual)], were significantly higher than those obtained with FCM clustering [CBF(fuzzy)]. This may have been due to the contamination of non-arterial pixels into the manually drawn ROI, as suggested by simulation 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 appears promising for determination of AIF because it allows automatic, rapid and accurate extraction of arterial pixels. (C) 2001 Wiley-Liss, Inc.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 26/01/20 alle ore 22:44:41