Literature DB >> 21924447

[Studying cerebral perfusion using magnetic susceptibility techniques: technique and applications].

J A Guzmán-de-Villoria1, P Fernández-García, J M Mateos-Pérez, M Desco.   

Abstract

Perfusion MRI makes it possible to evaluate the cerebral microvasculature through changes in signal due to a tracer passing through blood vessels. The most commonly used technique is based on the magnetic susceptibility of gadolinium in T2*-weighted sequences, and the most commonly evaluated parameters are cerebral blood volume, cerebral blood flow, and mean transit time. Diverse technical aspects, like the sequence used, and the dose and speed of contrast material injection, must be taken into account in perfusion MRI studies. It is also essential to consider possible sources of error like contrast material leaks due to changes in the permeability of the blood-brain barrier. The most widely used clinical applications of perfusion MRI include the determination of the degree of aggressiveness of gliomas, the differentiation of some histological types of tumors or pseudotumors, and the evaluation of the penumbral area in acute ischemia.
Copyright © 2011 SERAM. Published by Elsevier Espana. All rights reserved.

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Year:  2011        PMID: 21924447     DOI: 10.1016/j.rx.2011.06.003

Source DB:  PubMed          Journal:  Radiologia        ISSN: 0033-8338


  5 in total

1.  An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images.

Authors:  Siamak P Nejad-Davarani; Hassan Bagher-Ebadian; James R Ewing; Douglas C Noll; Tom Mikkelsen; Michael Chopp; Quan Jiang
Journal:  NMR Biomed       Date:  2017-02-17       Impact factor: 4.044

2.  Automatic determination of the arterial input function in dynamic susceptibility contrast MRI: comparison of different reproducible clustering algorithms.

Authors:  Jiandong Yin; Jiawen Yang; Qiyong Guo
Journal:  Neuroradiology       Date:  2015-01-30       Impact factor: 2.804

3.  Comparison of K-means and fuzzy c-means algorithm performance for automated determination of the arterial input function.

Authors:  Jiandong Yin; Hongzan Sun; Jiawen Yang; Qiyong Guo
Journal:  PLoS One       Date:  2014-02-04       Impact factor: 3.240

4.  Evaluating the feasibility of an agglomerative hierarchy clustering algorithm for the automatic detection of the arterial input function using DSC-MRI.

Authors:  Jiandong Yin; Jiawen Yang; Qiyong Guo
Journal:  PLoS One       Date:  2014-06-16       Impact factor: 3.240

5.  Detection of severity in Alzheimer's disease (AD) using computational modeling.

Authors:  Hyunjo Kim
Journal:  Bioinformation       Date:  2018-05-31
  5 in total

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