Cart (Loading....) | Create Account
Close category search window

Multilevel computed hemodynamic parameter maps from dynamic perfusion MRI

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)

A rapid time-series of T2*-weighted magnetic resonance (MR) images acquired during the first pass of a bolus injection of a paramagnetic MR contrast agent provides a basis for estimating cerebral perfusion. The concentration versus time of the contrast agent is calculated in a single image plane on a pixel-wise basis. A number of hemodynamic parameters can be extracted from the integral of the concentration-time curve, hence a method for obtaining an efficient and accurate estimate of the area under the curve is essential for the assessment of perfusion images. A new, automatic, multilevel processing method for calculating area under the concentration-time curve is proposed. In the first level of processing, the signal-to-noise ratio (SNR) is estimated for every pixel, and, if satisfactory, the limit points of integration are determined. An averaging filter is applied on the concentration-time curve, and the slope of the output of the filter Is calculated. The thresholds for initial and final integration points are determined next and are used to obtain limit points of integration. At this level, some pixels may be left without computed limits. These are determined at the next level of processing, using the median filter applied on the pixel neighborhood limit points. After determining limit points, the conventional method for integration is used to obtain the area under the curve. Tests of the proposed method are described for both simulated and real data. The simulated data are based on the gamma-variate function, with white noise added. The parameters used are obtained from fitting the gamma-variate function for regions within white matter, gray matter and cerebrospinal fluid in real perfusion images. The results obtained by the proposed estimator compare very favorably with the theoretical values for the particular cases studied. The performance of the proposed method for measuring cerebral hemodynamic parameters is also discussed when applied to perfusion studies in patients performed on a GE Signa 1.5 T, Advantage, whole body scanner. The results compare well with a previously used procedure based on slice-averaged time of arrival of the contrast bolus and manually determined limiting points. The new method automatizes the integration limit selection procedure efficiently, and is based on local, rather than global calculations. The procedure is simple and fast, and can perform numerical integration of 128*256 pMRI images in less than a second on a Sun SPARC 2 workstation

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:48 ,  Issue: 3 )

Date of Publication:

Jun 1999

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.