NDLI: Cerebral perfusion maps from dynamic contrast MRI data utilizing Rician statistics

Content Provider	IEEE Xplore Digital Library
Author	Fitzgerald, N. O'Sullivan, F. Newman, G.
Copyright Year	2009
Description	Author affiliation: Dept. Neurology, Albert Einstein Medical Center, Philadelphia, USA (Newman, G.) \|\| Dept of Statistics, UCC, Cork, Ireland (Fitzgerald, N.; O'Sullivan, F.)
Abstract	Bolus tracking of contrast agent with MRI is a well established technique for measurement of local cerebral hemodynamic parameters flow, volume and mean transit time. When performed on a voxel-by-voxel basis, it allows development of hemodynamic parameter maps useful for assessment of ischemic damage following stroke and tumor characterization in cancer. The analysis of the acquired dynamic data requires the use of deconvolution to reconstruct the residue function (R) of the contrast agent. Measurement of the tissue time course and the arterial input function are obtained by T2 or T2∗ weighted sequences. Reconstruction of R provides estimates of flow, volume and mean transit time. The raw MRI scan signal intensity is well approximated by Rician statistics. The standard approach to estimation involves logarithmic transformation and least squares deconvolution. At low signal to noise ratio this approach is not efficient and as an alternative this work adopts an iterative re-weighted non-linear least squares (IRWNLLS) algorithm to incorporate Rician statistics, impose constraints on the residue function and optimize for tracer arrival delay. The algorithm is implemented on a voxel-by-voxel basis and cerebral maps for the hemodynamic parameters flow, volume and mean transit time are presented. In addition, an automatic segmentation technique which takes into account both spatial and temporal variation is presented. This segmentation technique is shape driven, choosing only voxels that correlate highly with a well-known arterial input function template.
Starting Page	3840
Ending Page	3844
File Size	506733
Page Count	5
File Format	PDF
ISBN	9781424439614
ISSN	10957863
DOI	10.1109/NSSMIC.2009.5401910
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic resonance imaging Rician channels Statistics Hemodynamics Time measurement Deconvolution Least squares approximation Iterative algorithms Fluid flow measurement Volume measurement maximum likelihood perfusion MRI Rice distribution iterative re-weighted non-linear least squares
Content Type	Text
Resource Type	Article

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