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Abstract #0551

Improving Arterial Spin Labeling using Deep Learning

Ki Hwan Kim1, Seung Hong Choi2, and Sung-Hong Park1

1Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejeon, Republic of Korea, 2Department of Radiology, Seoul National University College of Medicine, Seoul, Korea, Democratic People's Republic of

We proposed a new convolutional neural network (CNN) framework to quantify cerebral blood flow (CBF) in Hadamard-encoded pseudo-continuous arterial spin labeling (HE-pCASL). Improving sensitivity and robustness in ASL signals allows CNNs to quantify CBF accurately with a smaller number of data acquisitions. The proposed methods outperformed the conventional averaging method in both normal and pathologic regions. Therefore, CNNs can be a good alternative to quantify CBF in ASL imaging.

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