IDEAS home Printed from https://ideas.repec.org/a/taf/gcmbxx/v20y2017i12p1350-1363.html
   My bibliography  Save this article

A combination of experimental measurement, constitutive damage model, and diffusion tensor imaging to characterize the mechanical properties of the human brain

Author

Listed:
  • Alireza Karimi
  • Seyed Mohammadali Rahmati
  • Reza Razaghi

Abstract

Understanding the mechanical properties of the human brain is deemed important as it may subject to various types of complex loadings during the Traumatic Brain Injury (TBI). Although many studies so far have been conducted to quantify the mechanical properties of the brain, there is a paucity of knowledge on the mechanical properties of the human brain tissue and the damage of its axon fibers under the various types of complex loadings during the Traumatic Brain Injury (TBI). Although many studies so far have been conducted to quantify the mechanical properties of the brain, there is a paucity of knowledge on the mechanical properties of the human brain tissue and the damage of its axon fibers under the frontal lobe of the human brain. The constrained nonlinear minimization method was employed to identify the brain coefficients according to the axial and transversal compressive data. The pseudo-elastic damage model data was also well compared with that of the experimental data and it not only up to the primary loading but also the discontinuous softening could well address the mechanical behavior of the brain tissue.

Suggested Citation

  • Alireza Karimi & Seyed Mohammadali Rahmati & Reza Razaghi, 2017. "A combination of experimental measurement, constitutive damage model, and diffusion tensor imaging to characterize the mechanical properties of the human brain," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 20(12), pages 1350-1363, September.
    • Handle: RePEc:taf:gcmbxx:v:20:y:2017:i:12:p:1350-1363
    DOI: 10.1080/10255842.2017.1362694
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10255842.2017.1362694
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10255842.2017.1362694?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. B.K. Wicker & H.P. Hutchens & Q. Wu & A.T. Yeh & J.D. Humphrey, 2008. "Normal basilar artery structure and biaxial mechanical behaviour," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 11(5), pages 539-551.
    2. Zhen Ma & João Manuel R.S. Tavares & Renato Natal Jorge & T. Mascarenhas, 2010. "A review of algorithms for medical image segmentation and their applications to the female pelvic cavity," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(2), pages 235-246.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Colleen M. Witzenburg & Victor H. Barocas, 2016. "A nonlinear anisotropic inverse method for computational dissection of inhomogeneous planar tissues," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 19(15), pages 1630-1646, November.
    2. Dimitrios P. Sokolis, 2014. "Identification and characterisation of regional variations in the material properties of ureter according to microstructure," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 17(15), pages 1653-1670, November.
    3. Jorge Barbosa & Bruno Figueiredo & Nuno Bettencourt & João Tavares, 2011. "Towards automatic quantification of the epicardial fat in non-contrasted CT images," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 14(10), pages 905-914.
    4. Abdul Momin & Naoshi Kondo & Dimas Firmanda Al Riza & Yuichi Ogawa & David Obenland, 2023. "A Methodological Review of Fluorescence Imaging for Quality Assessment of Agricultural Products," Agriculture, MDPI, vol. 13(7), pages 1-14, July.
    5. Xiangbin Liu & Liping Song & Shuai Liu & Yudong Zhang, 2021. "A Review of Deep-Learning-Based Medical Image Segmentation Methods," Sustainability, MDPI, vol. 13(3), pages 1-29, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:gcmbxx:v:20:y:2017:i:12:p:1350-1363. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/gcmb .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.