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Abstract #2651

Deep Learning Augmented Cerebral Blood Flow Measurement Using Arterial Spin Labeling Technique in Moyamoya Disease Before and After Direct Bypass Surgery

David Yen-Ting Chen1,2, Yosuke Ishii1,3, Jia Guo4, Audrey Peiwen Fan1, and Greg Zaharchuk1

1Radiology, Stanford University, Palo Alto, CA, United States, 2Medical Imaging, Shuan-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, 3Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan, 4Bioengineering, University of California Riverside, Riverside, CA, United States

We used single-delayed (SD) pseudo-continuous arterial spin labeling (PCASL), multi-delay (MD) ASL and a new, synthesized (Synth) ASL to longitudinally monitor cerebral blood flow (CBF) before and after direct bypass surgery in Moyamoya disease. The Synth-ASL was generated from a deep convolutional neural network, previously trained on a simultaneous [15O]-water PET/MRI dataset to generate a PET-like CBF map from MRI inputs. The Synth-ASL demonstrated a more homogenous CBF change across the brain and significantly greater CBF increase globally and regionally than SD-ASL and MD-ASL after surgery. Synth-ASL reduces bias in long arterial delay and measurement noise, and may enable robust CBF imaging follow-up in cerebrovascular patients.

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