Multi-modal inter-subject registration of mouse brain images. - Xia Li; Thomas E. Yankeelov; Glenn D. Rosen; John C. Gore; Benoit M. Dawant (Image Processing, 2006)

Constrained non-rigid registration for whole body image registration: method and validation. - Xia Li; Thomas E. Yankeelov; Todd E. Peterson 002; John C. Gore; Benoit M. Dawant (Image Processing, 2007)

Nonrigid registration algorithm for longitudinal breast MR images and the preliminary analysis of breast tumor response. - Xia Li; Benoit M. Dawant; E. Brian Welch; A. Bapsi Chakravarthy; Darla Freehardt; Ingrid A. Mayer; Mark C. Kelley; Ingrid M. Meszoely; John C. Gore; Thomas E. Yankeelov (Image Processing, 2009)

Implementation of a Semi-automated Post-processing System for Parametric MRI Mapping of Human Breast Cancer. - Robert E. Lee; E. Brian Welch; Jared G. Cobb; Tuhin Sinha; John C. Gore; Thomas E. Yankeelov (J. Digit. Imaging, 2009)

Early DCE-MRI Changes after Longitudinal Registration May Predict Breast Cancer Response to Neoadjuvant Chemotherapy. - Xia Li; Lori R. Arlinghaus; A. Bapsi Chakravarthy; Jaime Farley; Ingrid A. Mayer; Vandana G. Abramson; Mark C. Kelley; Ingrid M. Meszoely; Julie Means-Powell; Thomas E. Yankeelov (WBIR, 2012)

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. - David S. Smith; John C. Gore; Thomas E. Yankeelov; E. Brian Welch (Int. J. Biomed. Imaging, 2012)

Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Undersampling Patterns on Derived Parameters for DCE- and DSC-MRI. - David S. Smith; Xia Li; James V. Gambrell; Lori R. Arlinghaus; Christopher Chad Quarles; Thomas E. Yankeelov; E. Brian Welch (IEEE Trans. Medical Imaging, 2012)

Curvelets as a sparse basis for compressed sensing magnetic resonance imaging. - David S. Smith; Lori R. Arlinghaus; Thomas E. Yankeelov; Edward B. Welch (Image Processing, 2013)

Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Data to Constrain a Positron Emission Tomography Kinetic Model: Theory and Simulations. - Jacob U. Fluckiger; Xia Li; Jennifer G. Whisenant; Todd E. Peterson 002; John C. Gore; Thomas E. Yankeelov (Int. J. Biomed. Imaging, 2013)

Research and applications: Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. - Subramani Mani; Yukun Chen 001; Xia Li; Lori R. Arlinghaus; A. Bapsi Chakravarthy; Vandana G. Abramson; Sandeep R. Bhave; Mia A. Levy; Hua Xu 001; Thomas E. Yankeelov (J. Am. Medical Informatics Assoc., 2013)

Techniques and applications of dynamic contrast enhanced magnetic resonance imaging in cancer. - Stephanie L. Barnes; Jennifer G. Whisenant; Xia Li; Thomas E. Yankeelov (EMBC, 2014)

Image to physical space registration of supine breast MRI for image guided breast surgery. - Rebekah H. Conley; Ingrid M. Meszoely; Thomas S. Pheiffer; Jared A. Weis; Thomas E. Yankeelov; Michael I. Miga (Image-Guided Procedures, 2014)

Voxel-level reproducibility assessment of modality independent elastography in a pre-clinical murine model. - Katelyn M. Flint; Jared A. Weis; Thomas E. Yankeelov; Michael I. Miga (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2015)

Development of a diaphragmatic motion-based elastography framework for assessment of liver stiffness. - Jared A. Weis; Allison M. Johnsen; Geoffrey E. Wile; Thomas E. Yankeelov; Richard G. Abramson; Michael I. Miga (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2015)

Realization of a biomechanical model-assisted image guidance system for breast cancer surgery using supine MRI. - Rebekah H. Conley; Ingrid M. Meszoely; Jared A. Weis; Thomas S. Pheiffer; Lori R. Arlinghaus; Thomas E. Yankeelov; Michael I. Miga (Int. J. Comput. Assist. Radiol. Surg., 2015)

Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data. - Joseph B. DeGrandchamp; Jennifer G. Whisenant; Lori R. Arlinghaus; Vandana G. Abramson; Thomas E. Yankeelov; Julio Cárdenas-Rodríguez (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2016)

Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast. - Dong Wang 009; Lori R. Arlinghaus; Thomas E. Yankeelov; Xiaoping Yang 001; David S. Smith (Int. J. Biomed. Imaging, 2017)

Simulating the spread of COVID-19 via spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion. - Alex Viguerie; Guillermo Lorenzo; Ferdinando Auricchio; Davide Baroli; Thomas J. R. Hughes; Alessia Patton; Alessandro Reali; Thomas E. Yankeelov; Alessandro Veneziani (CoRR, 2020)

Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. - Chengyue Wu; David A. Hormuth; Todd A. Oliver; Federico Pineda; Guillermo Lorenzo; Gregory S. Karczmar; Robert D. Moser; Thomas E. Yankeelov (IEEE Trans. Medical Imaging, 2020)

Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion. - Alex Viguerie; Guillermo Lorenzo; Ferdinando Auricchio; Davide Baroli; Thomas J. R. Hughes; Alessia Patton; Alessandro Reali; Thomas E. Yankeelov; Alessandro Veneziani (Appl. Math. Lett., 2021)

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics. - Mirae Kim; Soonwoo Hong; Thomas E. Yankeelov; Hsin-Chih Yeh; Yen-Liang Liu (Bioinform., 2021)

Quantitative in vivo imaging to enable tumor forecasting and treatment optimization. - Guillermo Lorenzo; David A. Hormuth II; Angela M. Jarrett; Ernesto A. B. F. Lima; Shashank Subramanian; George Biros; J. Tinsley Oden; Thomas J. R. Hughes; Thomas E. Yankeelov (CoRR, 2021)

An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom. - Chengyue Wu; David A. Hormuth II; Ty Easley; Victor Eijkhout; Federico Pineda; Gregory S. Karczmar; Thomas E. Yankeelov (Medical Image Anal., 2021)

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth. - Ernesto A. B. F. Lima; Danial Faghihi; Russell Philley; Jianchen Yang; John Virostko; Caleb M. Phillips; Thomas E. Yankeelov (PLoS Comput. Biol., 2021)

Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging. - Birkan Tunç; David A. Hormuth II; George Biros; Thomas E. Yankeelov (IEEE Trans. Biomed. Eng., 2021)

Identifying mechanisms driving the early response of triple negative breast cancer patients to neoadjuvant chemotherapy using a mechanistic model integrating in vitro and in vivo imaging data. - Guillermo Lorenzo; Angela M. Jarrett; Christian T. Meyer; Vito Quaranta; Darren R. Tyson; Thomas E. Yankeelov (CoRR, 2022)

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules. - Grant R. Howard; Tyler A. Jost; Thomas E. Yankeelov; Amy Brock (PLoS Comput. Biol., 2022)

Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics. - Chengyue Wu; David A. Hormuth; Guillermo Lorenzo; Angela M. Jarrett; Federico Pineda; Frederick M. Howard; Gregory S. Karczmar; Thomas E. Yankeelov (IEEE Trans. Biomed. Eng., 2022)

Optimized Patient-Specific Catheter Placement for Convection-Enhanced Nanoparticle Delivery in Recurrent Glioblastoma. - Chengyue Wu; David A. Hormuth; Chase Christenson; Ryan T. Woodall; Michael R. A. Abdelmalik; William T. Phillips; Thomas J. R. Hughes; Andrew J. Brenner; Thomas E. Yankeelov (SC Workshops, 2023)

Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data. - Guillermo Lorenzo; Syed Rakin Ahmed; David A. Hormuth II; Brenna Vaughn; Jayashree Kalpathy-Cramer; Luis Solorio; Thomas E. Yankeelov; Héctor Gómez (CoRR, 2023)

Patient-specific computational forecasting of prostate cancer growth during active surveillance using an imaging-informed biomechanistic model. - Guillermo Lorenzo; Jon S. Heiselman; Michael A. Liss; Michael I. Miga; Héctor Gómez; Thomas E. Yankeelov; Alessandro Reali; Thomas J. R. Hughes (CoRR, 2023)

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis. - Caleb M. Phillips; Ernesto A. B. F. Lima; Manasa Gadde; Angela M. Jarrett; Marissa Nichole Rylander; Thomas E. Yankeelov (PLoS Comput. Biol., 2023)

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. - Anirban Chaudhuri; Graham Pash; David A. Hormuth; Guillermo Lorenzo; Michael G. Kapteyn; Chengyue Wu; Ernesto A. B. F. Lima; Thomas E. Yankeelov; Karen Willcox (Frontiers Artif. Intell., 2023)

Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting. - Caleb M. Phillips; Ernesto A. B. F. Lima; Chengyue Wu; Angela M. Jarrett; Z. Zhou; N. Elshafeey; J. Ma; Greta Rauch; Thomas E. Yankeelov (J. Comput. Sci., 2023)

Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth. - Baoshan Liang; Jingye Tan; Luke Lozenski; David A. Hormuth; Thomas E. Yankeelov; Umberto Villa; Danial Faghihi (IEEE Trans. Medical Imaging, 2023)

Image-guided patient-specific optimization of catheter placement for convection-enhanced nanoparticle delivery in recurrent glioblastoma. - Chengyue Wu; David A. Hormuth; Chase Christenson; Ryan T. Woodall; Michael R. A. Abdelmalik; William T. Phillips; Thomas J. R. Hughes; Andrew J. Brenner; Thomas E. Yankeelov (Comput. Biol. Medicine, 2024)

Toward mechanistic medical digital twins: some use cases in immunology. - Reinhard C. Laubenbacher; Fred Adler; Gary An; Filippo Castiglione; Stephen Eubank 002; Luis L. Fonseca; James A. Glazier; Tomás Helikar; Marti Jett-Tilton; Denise Kirschner; Paul Macklin; Borna Mehrad; et al. (Frontiers Digit. Health, 2024)

Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition. - Chase Christenson; Chengyue Wu; David A. Hormuth; Casey E. Stowers; Megan LaMonica; Jingfei Ma; Gaiane M. Rauch; Thomas E. Yankeelov (J. Comput. Sci., 2024)

Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States. - Orhun O. Davarci; Emily Y. Yang; Alexander Viguerie; Thomas E. Yankeelov; Guillermo Lorenzo (Eng. Comput., 2024)

A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data. - Guillermo Lorenzo; Angela M. Jarrett; Christian T. Meyer; Julie C. DiCarlo; John Virostko; Vito Quaranta; Darren R. Tyson; Thomas E. Yankeelov (Eng. Comput., 2024)

MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens. - Chengyue Wu; Ernesto A. B. F. Lima; Casey E. Stowers; Zhan Xu; Clinton Yam; Jong Bum Son; Jingfei Ma; Gaiane M. Rauch; Thomas E. Yankeelov (npj Digit. Medicine, 2025)

Validating the predictions of mathematical models describing tumor growth and treatment response. - Guillermo Lorenzo; David A. Hormuth II; Chengyue Wu; Graham Pash; Anirban Chaudhuri; Ernesto A. B. F. Lima; Lois C. Okereke; Reshmi Patel; Karen Willcox; Thomas E. Yankeelov (CoRR, 2025)

TumorTwin: A python framework for patient-specific digital twins in oncology. - Michael G. Kapteyn; Anirban Chaudhuri; Ernesto A. B. F. Lima; Graham Pash; Rafael Bravo; Karen Willcox; Thomas E. Yankeelov; David A. Hormuth II (CoRR, 2025)

Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology. - Graham Pash; Umberto Villa; David A. Hormuth II; Thomas E. Yankeelov; Karen Willcox (CoRR, 2025)