Literature DB >> 17278586

Cerebrospinal fluid flow in the normal and hydrocephalic human brain.

Andreas A Linninger1, Michalis Xenos, David C Zhu, MahadevaBharath R Somayaji, Srinivasa Kondapalli, Richard D Penn.   

Abstract

Advances in magnetic resonance (MR) imaging techniques enable the accurate measurements of cerebrospinal fluid (CSF) flow in the human brain. In addition, image reconstruction tools facilitate the collection of patient-specific brain geometry data such as the exact dimensions of the ventricular and subarachnoidal spaces (SAS) as well as the computer-aided reconstruction of the CSF-filled spaces. The solution of the conservation of CSF mass and momentum balances over a finite computational mesh obtained from the MR images predict the patients' CSF flow and pressure field. Advanced image reconstruction tools used in conjunction with first principles of fluid mechanics allow an accurate verification of the CSF flow patters for individual patients. This paper presents a detailed analysis of pulsatile CSF flow and pressure dynamics in a normal and hydrocephalic patient. Experimental CSF flow measurements and computational results of flow and pressure fields in the ventricular system, the SAS and brain parenchyma are presented. The pulsating CSF motion is explored in normal and pathological conditions of communicating hydrocephalus. This paper predicts small transmantle pressure differences between lateral ventricles and SASs (approximately 10 Pa). The transmantle pressure between ventricles and SAS remains small even in the hydrocephalic patient (approximately 30 Pa), but the ICP pulsatility increases by a factor of four. The computational fluid dynamics (CFD) results of the predicted CSF flow velocities are in good agreement with Cine MRI measurements. Differences between the predicted and observed CSF flow velocities in the prepontine area point towards complex brain-CSF interactions. The paper presents the complete computational model to predict the pulsatile CSF flow in the cranial cavity.

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Year:  2007        PMID: 17278586     DOI: 10.1109/TBME.2006.886853

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  30 in total

1.  A mathematical model of blood, cerebrospinal fluid and brain dynamics.

Authors:  Andreas A Linninger; Michalis Xenos; Brian Sweetman; Sukruti Ponkshe; Xiaodong Guo; Richard Penn
Journal:  J Math Biol       Date:  2009-02-15       Impact factor: 2.259

2.  Pulsatile flow in ventricular catheters for hydrocephalus.

Authors:  Á Giménez; M Galarza; U Thomale; M U Schuhmann; J Valero; J M Amigó
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2017-06-28       Impact factor: 4.226

3.  Brain hypothermia induced by cold spinal fluid using a torso cooling pad: theoretical analyses.

Authors:  Katisha D Smith; Liang Zhu
Journal:  Med Biol Eng Comput       Date:  2010-06-04       Impact factor: 2.602

4.  Limits to flow detection in phase contrast MRI.

Authors:  Nathan H Williamson; Michal E Komlosh; Dan Benjamini; Peter J Basser
Journal:  J Magn Reson Open       Date:  2020-07-22

5.  Three-dimensional computational prediction of cerebrospinal fluid flow in the human brain.

Authors:  Brian Sweetman; Michalis Xenos; Laura Zitella; Andreas A Linninger
Journal:  Comput Biol Med       Date:  2011-01-07       Impact factor: 4.589

6.  Automatic recognition of subject-specific cerebrovascular trees.

Authors:  Chih-Yang Hsu; Ben Schneller; Ali Alaraj; Michael Flannery; Xiaohong Joe Zhou; Andreas Linninger
Journal:  Magn Reson Med       Date:  2016-01-17       Impact factor: 4.668

7.  Inter-operator Reliability of Magnetic Resonance Image-Based Computational Fluid Dynamics Prediction of Cerebrospinal Fluid Motion in the Cervical Spine.

Authors:  Bryn A Martin; Theresia I Yiallourou; Soroush Heidari Pahlavian; Suraj Thyagaraj; Alexander C Bunck; Francis Loth; Daniel B Sheffer; Jan Robert Kröger; Nikolaos Stergiopulos
Journal:  Ann Biomed Eng       Date:  2015-10-07       Impact factor: 3.934

Review 8.  Methods to measure, model and manipulate fluid flow in brain.

Authors:  Krishnashis Chatterjee; Cora M Carman-Esparza; Jennifer M Munson
Journal:  J Neurosci Methods       Date:  2019-12-12       Impact factor: 2.390

9.  An impedance sensor to monitor and control cerebral ventricular volume.

Authors:  Andreas Linninger; Sukhraaj Basati; Robert Dawe; Richard Penn
Journal:  Med Eng Phys       Date:  2009-05-05       Impact factor: 2.242

10.  Development of a theoretical framework for analyzing cerebrospinal fluid dynamics.

Authors:  Benjamin Cohen; Abram Voorhees; Søren Vedel; Timothy Wei
Journal:  Cerebrospinal Fluid Res       Date:  2009-09-22
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