Dr. Thomas E. Yankeelov
Professor at Univ of Texas at Austin
SPIE Involvement:
Author | Instructor
Publications (20)

SPIE Journal Paper | 8 March 2024
Chengyue Wu, David Hormuth, Ty Easley, Federico Pineda, Gregory Karczmar, Thomas Yankeelov
JMI, Vol. 11, Issue 02, 024002, (March 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.024002
KEYWORDS: Tumors, Signal to noise ratio, Spatial resolution, Hemodynamics, Image segmentation, Image resolution, Temporal resolution, Morphological analysis, Cancer, Biological imaging

Proceedings Article | 1 March 2019 Paper
Proceedings Volume 10948, 109483M (2019) https://doi.org/10.1117/12.2512867
KEYWORDS: Single photon emission computed tomography, Calibration, Data modeling, Tumors, Magnetic resonance imaging, Brain, Mathematical modeling, Diffusion, Tissues

SPIE Journal Paper | 22 January 2018
Anna Sorace, Savannah Partridge, Xia Li, Jack Virostko, Stephanie Barnes, Daniel Hippe, Wei Huang, Thomas Yankeelov
JMI, Vol. 5, Issue 01, 011019, (January 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011019
KEYWORDS: Magnetic resonance imaging, Breast, Diagnostics, Data modeling, Tumors, Tumor growth modeling, Temporal resolution, Biopsy, Breast cancer, Data acquisition

SPIE Journal Paper | 29 December 2017
Hakmook Kang, Allison Hainline, Lori R. Arlinghaus, Stephanie Elderidge, Xia Li, Vandana G. Abramson, Anuradha Bapsi Chakravarthy, Richard G. Abramson, Brian Bingham, Kareem Fakhoury, Thomas E. Yankeelov
JMI, Vol. 5, Issue 01, 011015, (December 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011015
KEYWORDS: Receptors, Data modeling, Tumors, Magnetic resonance imaging, Breast cancer, Chromium, Statistical modeling, Cancer, Breast, Tumor growth modeling

SPIE Journal Paper | 24 November 2017 Open Access
John Virostko, Allison Hainline, Hakmook Kang, Lori R. Arlinghaus, Richard G. Abramson, Stephanie L. Barnes, Jeffrey D. Blume, Sarah Avery, Debra Patt, Boone Goodgame, Thomas E. Yankeelov, Anna G. Sorace
JMI, Vol. 5, Issue 01, 011011, (November 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011011
KEYWORDS: Magnetic resonance imaging, Breast cancer, Tumors, Diagnostics, Temporal resolution, Data modeling, Statistical analysis, Receptors, Error analysis, Cancer

Showing 5 of 20 publications
Course Instructor
SC938: Quantitative in vivo Imaging of Cancer
The course begins with a brief unit on the basic biological characteristics of cancer and then proceeds to study how each of the major <i>in vivo</i> imaging modalities is used to interrogate the tumor micro- and macroenvironment. The imaging techniques covered include: magnetic resonance imaging (MRI), optical imaging, computed tomography (CT), single photon emission computed tomography (SPECT), positron emission tomography (PET), and ultrasound imaging. A theme throughout the course is how imaging can go beyond mere anatomic/morphologic characterization to provide quantitative assessment of tumor growth and treatment response. As opposed to courses that offer an overview of particular imaging technologies, this course is specifically focused at understanding the application of the common imaging modalities to the problem of quantitatively characterizing of tumors. Extensive examples from both the pre-clinical and clinical settings will be presented.
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