Item Details

title

Estimation of Dynamic Contrast-Enhanced MRI Kinetic Parameters in Glioblastoma Using Deep Learning.

author

Sankepalle, Deeksha Maheswari

abstract

Glioblastoma (GBM) is one of the most aggressive cancers, which is commonly characterized by increased angiogenesis, hypercellularity, and disrupted blood-brain-barrier (BBB). Dynamic Contrast-Enhanced (DCE) MRI is an emerging non-invasive MR imaging technique to assess the invasiveness of the tumor by studying BBB. By fully automating the generation of kinetic parameter maps, we can reduce the amount of data produced and also eliminate user bias. This project compares two approaches to generate kinetic parameter maps: extended Tofts (Ex-Tofts) model and a deep learning network.

subject

CNN

DCE MRI

Deep learning

GBM

mice

Tofts

contributor

Zhao, Dawen (committee chair)

Weis, Jared (committee member)

Topaloglu, Umit (committee member)

date

2020-08-28T08:35:24Z (accessioned)

2021-08-27T08:30:14Z (available)

2020 (issued)

degree

Biomedical Engineering (discipline)

embargo

2021-08-27 (terms)

identifier

http://hdl.handle.net/10339/96946 (uri)

language

en (iso)

publisher

Wake Forest University

type

Thesis