Dr. Thomas E. Yankeelov Profile

Dr. Thomas E. Yankeelov

Professor at Univ of Texas at Austin

SPIE Involvement:

Author | Instructor

Publications (20)

SPIE Journal Paper | 8 March 2024

Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom

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

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Proceedings Article | 1 March 2019 Paper

Integrating quantitative imaging and computational modeling to predict the spatiotemporal distribution of 186Re nanoliposomes for recurrent glioblastoma treatment

Ryan Woodall, David Hormuth, Michael R. Abdelmalik, Chengyue Wu, Xinzeng Feng, William Phillips, Ande Bao, Thomas J. Hughes, Andrew Brenner, Thomas Yankeelov

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

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SPIE Journal Paper | 22 January 2018

Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial

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

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SPIE Journal Paper | 29 December 2017

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results

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

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SPIE Journal Paper | 24 November 2017 Open Access

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis

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

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SPIE Journal Paper | 30 October 2017 Open Access

Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies

Dariya Malyarenko, Andriy Fedorov, Laura Bell, Melissa Prah, Stefanie Hectors, Lori Arlinghaus, Mark Muzi, Meiyappan Solaiyappan, Michael Jacobs, Maggie Fung, Amita Shukla-Dave, Kevin McManus, Michael Boss, Bachir Taouli, Thomas Yankeelov, Christopher Chad Quarles, Kathleen Schmainda, Thomas Chenevert, David Newitt

JMI, Vol. 5, Issue 01, 011006, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011006

KEYWORDS: Diffusion weighted imaging, Diffusion, Digital imaging, Medicine, Data modeling, Data communications, MATLAB, Scanners, Standards development, Magnetic resonance imaging

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SPIE Journal Paper | 10 October 2017 Open Access

Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network

David Newitt, Dariya Malyarenko, Thomas Chenevert, C. Chad Quarles, Laura Bell, Andriy Fedorov, Fiona Fennessy, Michael Jacobs, Meiyappan Solaiyappan, Stefanie Hectors, Bachir Taouli, Mark Muzi, Paul Kinahan, Kathleen Schmainda, Melissa Prah, Erin Taber, Christopher Kroenke, Wei Huang, Lori Arlinghaus, Thomas Yankeelov, Yue Cao, Madhava Aryal, Yi-Fen Yen, Jayashree Kalpathy-Cramer, Amita Shukla-Dave, Maggie Fung, Jiachao Liang, Michael Boss, Nola Hylton

JMI, Vol. 5, Issue 01, 011003, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011003

KEYWORDS: Diffusion, In vivo imaging, Diffusion weighted imaging, Breast, MATLAB, Artificial intelligence, Magnetic resonance imaging, Radiology, Statistical analysis, Medicine

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Proceedings Article | 29 March 2016 Paper

Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data

Jennifer Whisenant, Thomas Yankeelov, Lori Arlinghaus, V. Abramson, Julio Cárdenas-Rodríguez, Joseph DeGrandchamp

Proceedings Volume 9788, 978811 (2016) https://doi.org/10.1117/12.2217008

KEYWORDS: Magnetic resonance imaging, Breast cancer, Signal to noise ratio, Data modeling, Temporal resolution, Tumor growth modeling, Receptors, Performance modeling, Breast imaging, Vascular imaging, Tumors, Tissues, Lawrencium

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SPIE Journal Paper | 2 July 2015

Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer

Jared Weis, Katelyn Flint, Violeta Sanchez, Thomas Yankeelov, Michael Miga

JMI, Vol. 2, Issue 03, 036001, (July 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.3.036001

KEYWORDS: Tumors, Elastography, Magnetic resonance imaging, Tissues, Cancer, Tumor growth modeling, Breast cancer, Collagen, Image compression, Image registration

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Proceedings Article | 19 March 2015 Paper

Voxel-level reproducibility assessment of modality independent elastography in a pre-clinical murine model

Katelyn Flint, Jared Weis, Thomas Yankeelov, Michael Miga

Proceedings Volume 9417, 941704 (2015) https://doi.org/10.1117/12.2082230

KEYWORDS: Elastography, Tumors, Tissues, Breast cancer, Tumor growth modeling, Image processing, Image compression, Cancer, Magnetic resonance elastography, Magnetic resonance imaging

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Proceedings Article | 19 March 2015 Paper

Development of a diaphragmatic motion-based elastography framework for assessment of liver stiffness

Allison Johnsen, Thomas Yankeelov, Richard Abramson, Geoffrey Wile, Jared Weis, Michael Miga

Proceedings Volume 9417, 941703 (2015) https://doi.org/10.1117/12.2081257

KEYWORDS: Liver, Tissues, Elastography, Motion models, Image segmentation, Magnetic resonance imaging, Magnetic resonance elastography, Model-based design, Reconstruction algorithms, Computer simulations

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Proceedings Article | 13 March 2014 Paper

Validation and reproducibility assessment of modality independent elastography in a pre-clinical model of breast cancer

Jared Weis, Dong Kim, Thomas Yankeelov, Michael Miga

Proceedings Volume 9038, 90381I (2014) https://doi.org/10.1117/12.2042796

KEYWORDS: Cancer, Tissues, Elastography, Breast cancer, Magnetic resonance imaging, Tumors, Tumor growth modeling, Image compression, Data modeling, Mechanics

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Proceedings Article | 13 March 2014 Paper

Utilizing a reference material for assessing absolute tumor mechanical properties in modality independent elastography

Dong Kyu Kim, Jared Weis, Thomas Yankeelov, Michael Miga

Proceedings Volume 9038, 90381F (2014) https://doi.org/10.1117/12.2044195

KEYWORDS: Tumors, Tissues, Elastography, Breast cancer, Silicon, Magnetic resonance imaging, Image segmentation, Cancer, Image registration, 3D image processing

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Proceedings Article | 12 March 2014 Paper

Image to physical space registration of supine breast MRI for image guided breast surgery

Rebekah Conley, Thomas Pheiffer, Michael Miga, Ingrid Meszoely, Jared Weis, Thomas Yankeelov

Proceedings Volume 9036, 90362N (2014) https://doi.org/10.1117/12.2043817

KEYWORDS: Breast, Tumors, Magnetic resonance imaging, Ultrasonography, Image registration, Surgery, Tissues, Clouds, Natural surfaces, Breast cancer

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Breast conservation therapy (BCT) is a desirable option for many women diagnosed with early stage breast cancer and involves a lumpectomy followed by radiotherapy. However, approximately 50% of eligible women will elect for mastectomy over BCT despite equal survival benefit (provided margins of excised tissue are cancer free) due to uncertainty in outcome with regards to complete excision of cancerous cells, risk of local recurrence, and cosmesis. Determining surgical margins intraoperatively is difficult and achieving negative margins is not as robust as it needs to be, resulting in high re-operation rates and often mastectomy. Magnetic resonance images (MRI) can provide detailed information about tumor margin extents, however diagnostic images are acquired in a fundamentally different patient presentation than that used in surgery. Therefore, the high quality diagnostic MRIs taken in the prone position with pendant breast are not optimal for use in surgical planning/guidance due to the drastic shape change between preoperative images and the common supine surgical position. This work proposes to investigate the value of supine MRI in an effort to localize tumors intraoperatively using image-guidance. Mock intraoperative setups (realistic patient positioning in non-sterile environment) and preoperative imaging data were collected from a patient scheduled for a lumpectomy. The mock intraoperative data included a tracked laser range scan of the patient's breast surface, tracked center points of MR visible fiducials on the patient's breast, and tracked B-mode ultrasound and strain images. The preoperative data included a supine MRI with visible fiducial markers. Fiducial markers localized in the MRI were rigidly registered to their mock intraoperative counterparts using an optically tracked stylus. The root mean square (RMS) fiducial registration error using the tracked markers was 3.4mm. Following registration, the average closest point distance between the MR generated surface nodes and the LRS point cloud was 1.76±0.502 mm.

Proceedings Article | 29 March 2013 Paper

A consistent pre-clinical/clinical elastography approach for assessing tumor mechanical properties in therapeutic systems

Michael Miga, Jared Weis, Lori Arlinghaus, Xia Li, Stephanie Barnes, Thomas Yankeelov, Samantha Munoz, Rahul Sastry

Proceedings Volume 8672, 86721F (2013) https://doi.org/10.1117/12.2007425

KEYWORDS: Elastography, Tumors, Tissues, Magnetic resonance imaging, Breast, Breast cancer, Model-based design, Image registration, Image segmentation, Imaging systems

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Proceedings Article | 29 March 2013 Paper

A mechanically coupled reaction diffusion model of breast tumor response during neoadjuvant chemotherapy

Xia Li, Lori Arlinghaus, A. Bapsi Chakravarthy, Vandana Abramson, Richard Abramson, Jaime Farley, Jared Weis, Michael Miga, Thomas Yankeelov

Proceedings Volume 8672, 86721G (2013) https://doi.org/10.1117/12.2007961

KEYWORDS: Tumors, Data modeling, Mathematical modeling, Diffusion, Magnetic resonance imaging, Breast, Tumor growth modeling, Mechanics, Image enhancement, In vivo imaging

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Proceedings Article | 13 March 2013 Paper

Curvelets as a sparse basis for compressed sensing magnetic resonance imaging

Thomas Yankeelov, Lori Arlinghaus, David Smith, Edward Welch

Proceedings Volume 8669, 866929 (2013) https://doi.org/10.1117/12.2007032

KEYWORDS: Compressed sensing, Magnetic resonance imaging, Wavelets, Image compression, Breast, Image quality, Spatial frequencies, Data acquisition, Image processing, JPEG2000

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Proceedings Article | 27 March 2009 Paper

Nonrigid registration algorithm for longitudinal breast MR images and the preliminary analysis of breast tumor response

Mark Kelley, Thomas Yankeelov, Benoit Dawant, E. Brian Welch, Xia Li, Ingrid Meszoely, Ingrid Mayer, A. Bapsi Chakravarthy, John Gore, Darla Freehardt

Proceedings Volume 7259, 72590Q (2009) https://doi.org/10.1117/12.811216

KEYWORDS: Tumors, Image registration, Breast, Magnetic resonance imaging, Detection and tracking algorithms, Tissues, Image resolution, Computer simulations, Mammography, Breast cancer

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Proceedings Article | 1 March 2007 Paper

Constrained non-rigid registration for whole body image registration: method and validation

Thomas Yankeelov, John Gore, Benoit Dawant, Todd Peterson, Xia Li

Proceedings Volume 6512, 651202 (2007) https://doi.org/10.1117/12.709026

KEYWORDS: Image registration, Image segmentation, Bone, Computed tomography, Tissues, Detection and tracking algorithms, Evolutionary algorithms, Lung, Image restoration, Head

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Proceedings Article | 10 March 2006 Paper

Multi-modal inter-subject registration of mouse brain images

Thomas Yankeelov, Glenn Rosen, Benoit Dawant, Xia Li, John Gore

Proceedings Volume 6144, 61440T (2006) https://doi.org/10.1117/12.652407

KEYWORDS: Brain, Image registration, Image segmentation, Magnetic resonance imaging, Neuroimaging, 3D image processing, Image processing, Algorithm development, Reconstruction algorithms, 3D image reconstruction

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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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