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Automatic selection of arterial input function using cluster analysis

Automatic selection of arterial input function using cluster analysis Quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast MRI requires determination of the arterial input function (AIF) representing the delivery of intravascular tracer to tissue. This is typically accomplished manually by inspection of concentration time curves (CTCs) in regions containing the ICA, VA, and MCA. This is, however, a time consuming and operator dependent procedure. We suggest a completely automatic procedure for establishing the AIF based on a cluster analysis algorithm. In 20 normal subjects CBF maps calculated in 2 slices by the automatic procedure were compared to maps obtained with AIFs selected individually by 7 experienced operators. The average manual to automatic CBF ratio was 1.03 ± 0.15 in the lower slice and 1.05 ± 0.12 in the upper slice, demonstrating excellent agreement between the manual and automatic method. The algorithm provides means for objectively assessing AIF candidates in local AIF search algorithms designed to reduce bias due to delay and dispersion. Given the reproducibility and speed (10 s) of the automatic method, we speculate that it will greatly improve the accuracy of perfusion images and facilitate their use in clinical diagnosis and decision‐making, particularly in acute stroke but also in cerebrovascular disease in general. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Magnetic Resonance in Medicine Wiley

Automatic selection of arterial input function using cluster analysis

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References (21)

Publisher
Wiley
Copyright
Copyright © 2006 Wiley Subscription Services
ISSN
0740-3194
eISSN
1522-2594
DOI
10.1002/mrm.20759
pmid
16453314
Publisher site
See Article on Publisher Site

Abstract

Quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast MRI requires determination of the arterial input function (AIF) representing the delivery of intravascular tracer to tissue. This is typically accomplished manually by inspection of concentration time curves (CTCs) in regions containing the ICA, VA, and MCA. This is, however, a time consuming and operator dependent procedure. We suggest a completely automatic procedure for establishing the AIF based on a cluster analysis algorithm. In 20 normal subjects CBF maps calculated in 2 slices by the automatic procedure were compared to maps obtained with AIFs selected individually by 7 experienced operators. The average manual to automatic CBF ratio was 1.03 ± 0.15 in the lower slice and 1.05 ± 0.12 in the upper slice, demonstrating excellent agreement between the manual and automatic method. The algorithm provides means for objectively assessing AIF candidates in local AIF search algorithms designed to reduce bias due to delay and dispersion. Given the reproducibility and speed (10 s) of the automatic method, we speculate that it will greatly improve the accuracy of perfusion images and facilitate their use in clinical diagnosis and decision‐making, particularly in acute stroke but also in cerebrovascular disease in general. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.

Journal

Magnetic Resonance in MedicineWiley

Published: Jan 1, 2006

Keywords: ; ; ; ;

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