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3 March 2007 Automated arterial input function identification using iterative self organizing maps
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Quantification of cerebral blood flow and volume using dynamic-susceptibility contrast MRI relies on deconvolution with the arterial input function (AIF) - commonly estimated from signal changes in a major artery. Manual selection of AIF is user-dependent and typical selection in primary arteries leads to errors due to bolus delay and dispersion. An AIF sampled form the primary as well as the peripheral arteries should minimize these errors. We present a fully automated technique for the identification of the AIF by classifying the pixels in the imaging set into unique classes using a Kohonen self organizing map, followed by an iterative refinement of the previous selections. Validation was performed across 31 pediatric patients by comparison with manually identified AIF and a recently published automated AIF technique. Our technique consistently yielded higher bolus peak heights and over 50% increase in the area under the first pass, therefore lowering the values obtained for blood flow and volume. This technique provides a robust and accurate estimation of the arterial input function and can easily be adapted to extract the AIF locally, regionally or globally as suitable to the analysis.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jinesh J. Jain, John O. Glass, and Wilburn E. Reddick "Automated arterial input function identification using iterative self organizing maps", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65120U (3 March 2007);

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