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Finite element modeling of decompressive craniectomy (DC) and its clinical validation
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
2015 (English)In: ADVANCES IN BIOMEDICAL SCIENCE AND ENGINEERING, Vol. 2, no 1, p. 1-9Article in journal (Refereed) Published
Abstract [en]

Decompressive craniectomy (DC) is a reliable neurosurgical approach to reduce a pathologically increased intracranial pressure after neurological diseases such as severe traumatic brain injury (TBI) and stroke. The procedure has substantially reduced the mortality rate but at the expense of increased neurological cognitive impairments. Finite Element (FE) modeling in the past decades has become an important tool to develop innovative treatment strategies in various areas of the clinical neuroscience field. The aim of this study was to develop patient-specific FE models to simulate DC surgery and validate the models against patients' clinical data. The FE models were created based on the Computed Tomography (CT) images of six patients treated with DC. Brain tissue was modeled as poroelastic material. To validate the model prediction, the motion of brain surface at the DC area from the simulation was compared with the measured values from medical images which were derived from image registration. The results from the computational simulations gave a reliable prediction of brain surface motion at DC area for all the six patients evaluated. Both the deformation pattern and the quantitative values of the brain surface displacement from the model simulation were found in good agreement with measured values from medical images. The developed FE model and its validation in this study is a prerequisite for future investigations aiming at finding optimal treatment for a specific patient which hopefully will significantly improve patients' outcome.

Place, publisher, year, edition, pages
2015. Vol. 2, no 1, p. 1-9
National Category
Medical Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-250746OAI: oai:DiVA.org:kth-250746DiVA, id: diva2:1347321
Note

QC 20190903

Available from: 2019-08-31 Created: 2019-08-31 Last updated: 2019-09-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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  • nn-NO
  • nn-NB
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More languages
Output format
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