Deep Learning Quantification of Vascular Permeability in Cancer
Electronic Theses and Dissertations
Item Files
Item Details
- title
- Deep Learning Quantification of Vascular Permeability in Cancer
- author
- Arledge, Chad
- abstract
- Magnetic resonance imaging (MRI) is a powerful noninvasive tool for assessing the tumor microenvironment. Advancements in MR techniques have increased noninvasive access to a significant amount of useful information on cancer metabolism and tumor heterogeneity, encompassing spatial, functional, and metabolic imaging. In particular, dynamic contrast enhanced (DCE) MRI has emerged as a quantitative standard for assessing tumor microvascular perfusion and permeability. Vascular permeability parameters derived from pharmacokinetic (PK) models applied to DCE MRI datasets have shown great promise for evaluating tumor vascular function and therapeutic response in both pre-clinical and clinical applications.
- subject
- Convolutional Neural Network
- Deep Learning
- Dynamic Contrast Enhanced MRI
- Ensemble Learning
- Generative Adversarial Network
- Vascular Permeability
- contributor
- Zhao, Dawen (advisor)
- Topaloglu, Umit (committee member)
- Bourland, J. Daniel (committee member)
- Chan, Michael D. (committee member)
- Niazi, Khalid (committee member)
- date
- 2025-06-24T08:36:39Z (accessioned)
- 2025 (issued)
- degree
- Biomedical Engineering (discipline)
- embargo
- 2025-12-23 (terms)
- 2025-12-23 (liftdate)
- identifier
- http://hdl.handle.net/10339/111050 (uri)
- language
- en (iso)
- publisher
- Wake Forest University
- type
- Dissertation