Abstract #4904

Parametric ATT and CBF Mapping Using a Three-Dimensional Convolutional Neural Network

Donghoon Kim^1,2, Megan Lipford³, Hongjian He⁴, Qiuping Ding⁴, Vladimir Ivanovic², Samuel Lockhart⁵, Suzanne Craft⁵, Christopher T. Whitlow³, and Youngkyoo Jung^1,2,3

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.

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