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Abstract #1070

Using Deep Learning to Predict PET Cerebrovascular Reserve in Moyamoya Disease from Baseline MRI

David Yen-Ting Chen1,2, Yosuke Ishii1,3, Moss Yize Zhao1, Audrey Peiwen Fan1, and Greg Zaharchuk1
1Radiology, Stanford University, Palo Alto, CA, United States, 2Medical Imaging, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, 3Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan

Cerebrovascular reserve (CVR) is an important hemodynamic parameter for moyamoya disease. Acetazolamide (ACZ) test is often used to measure CVR clinically. However, ACZ is contraindicated in patients with sulfa allergies, severe kidney and liver disease and potentially has severe adverse side effect. Thus, there is a need to assess CVR without pharmacological vasodilation. We utilized a simultaneous [15O]-water PET/MRI dataset to train a convolutional neural network (CNN) to predict CVR. The CNN combined multi-contrast information from baseline perfusion and structural images to predict whole-brain PET-level CVR, with high image quality, quantiļ¬cation accuracy, and diagnostic accuracy for identifying impaired CVR.

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