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Analysis of input functions from different arterial branches with gamma variate functions and cluster analysis for quantitative blood volume measurements

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84187

Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rausch, M., Scheffler, K., Rudin, M., & Radü, E. (2000). Analysis of input functions from different arterial branches with gamma variate functions and cluster analysis for quantitative blood volume measurements. Magnetic Resonance Imaging, 18(10), 1235-1243. Retrieved from http://www.sciencedirect.com/science/article/pii/S0730725X00002198.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E3D6-B
Abstract
Regional cerebral blood volume (rCBV) provides valuable information about the nature and progress of diseases of the central nervous system. While relative rCBV maps can be derived directly from dynamic susceptibility contrast data, the arterial input function (AIF) has to be measured for absolute rCBV quantification. For determination of the AIF pixels located completely within a feeding artery must be selected. However, by using a region-of-interest (ROI) based selection some confounding effects can occur, especially if single shot echo planar imaging (EPI) with low spatial resolution is used. In this study we analyzed the influence of partial volume effects and spatial misregistration due to frequency shifts induced by paramagnetic contrast agents. We analyzed AIFs from the internal carotid artery (ICA), the vertebral artery (VA) and the middle cerebral artery (MCA) using gamma variate function based parameterization. The concentration time curves (CTC) of several pixels which were selected on the basis of strong signal drop appeared distorted during the bolus passage. Moreover, the amplitudes of input functions derived from the MCA were smaller by a factor of three as compared to those of the ICA and VA. Simulations revealed that these effects can be attributed to a spatial shift of the vessel along phase-encoding direction during the passage of the bolus. We therefore developed a procedure for a pixel selection based on cluster analysis which classifies pixels according to the parameters of the fitted gamma variate functions. This approach accounted for misregistration of the vessel and yielded very consistent results for a group of normal subjects.