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Abstract #0930

Deep Learning Reconstruction Method for Improved Visualization of Hippocampal Anatomical Structures  

Patrick Quarterman1, Angela Lignelli2, Marc Lebel3, and Sachin Jambawalikar4
1GE Healthcare, New York, NY, United States, 2Radiology, Columbia University, New York, NY, United States, 3GE Healthcare, Calgary, AB, Canada, 4Columbia University, New York, NY, United States

The purpose of this study was to determine if deep learning reconstruction (DLRecon) method to reduce image noise could lead to improvement in in-vivo anatomical detail of the hippocampus structures without substantial increase in scan/exam time on a clinical 3T system. Evaluation of this new reconstruction technique was performed on a group of 5 volunteers with results indicating that higher resolution scans compared to current seizure protocol was free of imaging noise and led to higher confidence in identifying hippocampal key anatomical structures and temporal lobes.

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