David A. Hormuth, II

Research Scientist | Biomedical Engineering + Imaging Science > > Computational Oncology

An adjoint-based method for a linear mechanically-coupled tumor model: application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging


Journal article


Xinzeng Feng, D. Hormuth, T. Yankeelov
Computational Mechanics, 2018

Semantic Scholar DOI PubMed
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APA   Click to copy
Feng, X., Hormuth, D., & Yankeelov, T. (2018). An adjoint-based method for a linear mechanically-coupled tumor model: application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging. Computational Mechanics.


Chicago/Turabian   Click to copy
Feng, Xinzeng, D. Hormuth, and T. Yankeelov. “An Adjoint-Based Method for a Linear Mechanically-Coupled Tumor Model: Application to Estimate the Spatial Variation of Murine Glioma Growth Based on Diffusion Weighted Magnetic Resonance Imaging.” Computational Mechanics (2018).


MLA   Click to copy
Feng, Xinzeng, et al. “An Adjoint-Based Method for a Linear Mechanically-Coupled Tumor Model: Application to Estimate the Spatial Variation of Murine Glioma Growth Based on Diffusion Weighted Magnetic Resonance Imaging.” Computational Mechanics, 2018.


BibTeX   Click to copy

@article{xinzeng2018a,
  title = {An adjoint-based method for a linear mechanically-coupled tumor model: application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging},
  year = {2018},
  journal = {Computational Mechanics},
  author = {Feng, Xinzeng and Hormuth, D. and Yankeelov, T.}
}


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