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Thomas E Yankeelov

Showing results (1-10 of 271) with videos related to

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ISRN Biomathematics|August 6, 2013
Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of CancerThomas E Yankeelov
Tomography (Ann Arbor, Mich.)|March 12, 2019
The Quantitative Imaging Network: A Decade of AchievementThomas E Yankeelov
Current Medical Imaging Reviews|October 16, 2009
Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and ExamplesThomas E Yankeelov, John C Gore
Annals of Biomedical Engineering|August 6, 2016
Introduction to the Special Section on Clinical Applications of Multi-Scale ModelingThomas E Yankeelov, Michael I Miga
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research|October 29, 2009
Has quantitative multimodal imaging of treatment response arrived?Thomas E Yankeelov, John C Gore
Magnetic Resonance Imaging|August 18, 2012
Preface to the special issue on quantitative imaging in cancerThomas E Yankeelov, John C Gore
Magnetic Resonance in Medicine|October 31, 2007
Incorporating contrast agent diffusion into the analysis of DCE-MRI dataMartin Pellerin, Thomas E Yankeelov, Martin Lepage
Computational Mechanics|March 19, 2019
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 imagingXinzeng Feng, David A Hormuth, Thomas E Yankeelov
Nature Reviews. Clinical Oncology|August 13, 2014
Quantitative multimodality imaging in cancer research and therapyThomas E Yankeelov, Richard G Abramson, C Chad Quarles
IEEE Transactions on Bio-Medical Engineering|June 1, 2021
Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance ImagingBirkan Tunc, David Hormuth, George Biros, et al.
Pageof 28

Showing results (1-10 of 271) with videos related to

Sort By:
Pageof 28
ISRN Biomathematics|August 6, 2013
Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of CancerThomas E Yankeelov
Tomography (Ann Arbor, Mich.)|March 12, 2019
The Quantitative Imaging Network: A Decade of AchievementThomas E Yankeelov
Current Medical Imaging Reviews|October 16, 2009
Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and ExamplesThomas E Yankeelov, John C Gore
Annals of Biomedical Engineering|August 6, 2016
Introduction to the Special Section on Clinical Applications of Multi-Scale ModelingThomas E Yankeelov, Michael I Miga
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research|October 29, 2009
Has quantitative multimodal imaging of treatment response arrived?Thomas E Yankeelov, John C Gore
Magnetic Resonance Imaging|August 18, 2012
Preface to the special issue on quantitative imaging in cancerThomas E Yankeelov, John C Gore
Magnetic Resonance in Medicine|October 31, 2007
Incorporating contrast agent diffusion into the analysis of DCE-MRI dataMartin Pellerin, Thomas E Yankeelov, Martin Lepage
Computational Mechanics|March 19, 2019
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 imagingXinzeng Feng, David A Hormuth, Thomas E Yankeelov
Nature Reviews. Clinical Oncology|August 13, 2014
Quantitative multimodality imaging in cancer research and therapyThomas E Yankeelov, Richard G Abramson, C Chad Quarles
IEEE Transactions on Bio-Medical Engineering|June 1, 2021
Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance ImagingBirkan Tunc, David Hormuth, George Biros, et al.
Pageof 28