Meeting Banner
Abstract #2004

Quantitative Analysis of Glioblastoma Treatment Using Dynamic Relaxation Contrast MRI at 9.4T

Jia Guo1, Nanyan Zhu2, Yanping Sun3, Sabrina J. Gjerswold-Selleck4, Hong-Jian Wei5,6, Pavan S. Upadhyayula6,7, Angeliki Mela6,7, Cheng-Chia Wu5,6, Peter D. Canoll6,7, John T. Vaughan4, Douglas L. Rothman8, and Scott A. Small9
1Department of Psychiatry, and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States, 2Department of Biological Science, Columbia University, New York, NY, United States, 3Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, United States, 4Department of Biomedical Engineering, Columbia University, New York, NY, United States, 5Department of Radiation Oncology, Columbia University, New York, NY, United States, 6Columbia University Irving Medical Center, Columbia University, New York, NY, United States, 7Department of Pathology and Cell Biology, Columbia University, New York, NY, United States, 8Departments of Diagnostic Radiology, or Biomedical Engineering, Yale University, New Haven, CT, United States, 9Departments of Neurology, Radiology or Psychiatry, Columbia University, New York, NY, United States

This abstract describes a novel dynamic T2-relaxation contrast magnetic resonance imaging (DRC-MRI) protocol for mapping the cerebral perfusion dynamics in mice. We demonstrate how to quantify cerebral perfusion dynamics with the proposed DRC modeling, which combines features of both dynamic and the steady-state methods. Quantitative analysis on both simulated and in vivo experimental data are performed. We first validate the reliability of our DRC modeling protocol with healthy mice before we apply the protocol on a tumor treatment study. We are able to demonstrate its ability to model the treatment effect of Etoposide on Glioblastoma in mice.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here