Abstract #4904
Parametric ATT and CBF Mapping Using a Three-Dimensional Convolutional Neural Network
Donghoon Kim1,2, Megan Lipford3, Hongjian He4, Qiuping Ding4, Vladimir Ivanovic2, Samuel Lockhart5, Suzanne Craft5, Christopher T. Whitlow3, and Youngkyoo Jung1,2,3
1Biomedical Engineering, University of California, Davis, CA, United States, 2Radiology, University of California, Davis, CA, United States, 3Radiology, Wake Forest University, Winston-Salem, NC, United States, 4Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhejiang, China, 5Internal Medicine, Wake Forest University, Winston-Salem, NC, United States
Synopsis
A CNN algorithm was proposed to estimate ATT and CBF simultaneously, using multi-TI ASL, acquiring ASL images at multiple PLDs. Hierarchical structure of CNN was used to reduce the estimation error of ATT and CBF. The proposed method successfully estimated ATT and CBF maps using reduced numbers of TIs or averages with a higher accuracy than a conventional non-linear model fitting. The successful estimation of ATT and CBF using our H-CNN may allow a total scan time reduction with a smaller number of TIs or averages and improvements of image quality when a part of acquisition is corrupted.
This abstract and the presentation materials are available to members only;
a login is required.
Join Here
