Dr. Thomas E. Yankeelov
Professor at Univ of Texas at Austin
SPIE Involvement:
Author | Instructor
Publications (19)

Proceedings Article | 1 March 2019 Paper
Proc. SPIE. 10948, Medical Imaging 2019: Physics of Medical Imaging
KEYWORDS: Mathematical modeling, Data modeling, Tumors, Tissues, Calibration, Magnetic resonance imaging, Diffusion, Single photon emission computed tomography, Brain

SPIE Journal Paper | 22 January 2018
JMI Vol. 5 Issue 01
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
JMI Vol. 5 Issue 01
KEYWORDS: Receptors, Data modeling, Tumors, Magnetic resonance imaging, Breast cancer, Chromium, Statistical modeling, Cancer, Breast, Tumor growth modeling

SPIE Journal Paper | 24 November 2017
JMI Vol. 5 Issue 01
KEYWORDS: Magnetic resonance imaging, Breast cancer, Tumors, Diagnostics, Temporal resolution, Data modeling, Statistical analysis, Receptors, Error analysis, Cancer

SPIE Journal Paper | 30 October 2017
JMI Vol. 5 Issue 01
KEYWORDS: Diffusion weighted imaging, Diffusion, Digital imaging, Medicine, Data modeling, Data communications, MATLAB, Scanners, Standards development, Magnetic resonance imaging

Showing 5 of 19 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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