Simulating magnetic resonance images based on a model of tumor growth incorporating microenvironment. - Pamela R. Jackson; Andrea Hawkins-Daarud; Savannah C. Partridge; Paul E. Kinahan; Kristin R. Swanson (Image Perception, Observer Performance, and Technology Assessment, 2018)

Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs. - Sara Ranjbar; Kyle W. Singleton; Lee Curtin; Susan Christine Massey; Andrea Hawkins-Daarud; Pamela R. Jackson; Kristin R. Swanson (ABCD-NP@MICCAI, 2019)

Simulated Diffusion Weighted Images Based on Model-Predicted Tumor Growth. - Pamela R. Jackson; Andrea Hawkins-Daarud; Kristin R. Swanson (SASHIMI@MICCAI, 2020)

A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type. - Sara Ranjbar; Kyle W. Singleton; Pamela R. Jackson; Cassandra R. Rickertsen; Scott A. Whitmire; Kamala R. Clark-Swanson; Joseph Ross Mitchell; Kristin R. Swanson; Leland S. Hu (J. Digit. Imaging, 2020)

Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation. - Eric A. Stahlberg; Mohamed Abdel-Rahman; Boris Aguilar; Alireza Asadpoure; Robert A. Beckman; Lynn L. Borkon; Jeffrey N. Bryan; Colleen M. Cebulla; Young Hwan Chang; Ansu Chatterjee; Jun Deng; Sepideh Dolatshahi; et al. (Frontiers Digit. Health, 2022)

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm. - Lujia Wang 002; Hairong Wang; Fulvio D'Angelo; Lee Curtin; Christopher P. Sereduk; Gustavo De Leon; Kyle W. Singleton; Javier Urcuyo; Andrea Hawkins-Daarud; Pamela R. Jackson; Chandan Krishna; Richard S. Zimmerman; et al. (CoRR, 2024)

Digital twins for health: a scoping review. - Evangelia Katsoulakis; Qi Wang 021; Huanmei Wu; Leili Shahriyari; Richard Fletcher; Jinwei Liu; Luke Achenie; Hongfang Liu; Pamela R. Jackson; Ying Xiao; Tanveer F. Syeda-Mahmood; Richard Tuli; et al. (npj Digit. Medicine, 2024)

Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma. - Hairong Wang; Michael G. Argenziano; Hyunsoo Yoon; Deborah Boyett; Akshay Save; Petros Petridis; William Savage; Pamela R. Jackson; Andrea Hawkins-Daarud; Nhan L. Tran; Leland S. Hu; Kyle W. Singleton; et al. (npj Digit. Medicine, 2024)