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Abstract #2886

Real-time Personalized Acquisition Optimization: 30%-50% reconstruction improvements from a 10-second undersampling optimization

Ke Wang1,2, Enhao Gong2, Suchandrima Banerjee3, John M Pauly2, and Greg Zaharchunk4

1Department of Biomedical Engineering, Tsinghua University, Beijing, China, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 3GE Healthcare, Menlo Park, CA, United States, 4Department of Radiology, Stanford University, Stanford, CA, United States

Improved undersampling designs can effectively improve the acquisition and resulting reconstruction accuracy. However, existing undersampling optimization methods are time-consuming and the limited performance prevent their clinical applications. Here we proposed an improved undersampling trajectory optimization scheme to generate an optimized trajectory within seconds and apply for subsequent multi-contrast MRI datasets on a per-subject basis. By using a data-driven method combined with improved algorithm design, GPU acceleration and more efficient computation, the proposed method can optimize a trajectory within 5-10 seconds and achieve 30% - 50% reconstruction improvement with the same acquisition cost, which makes real-time under-sampling optimization possible for clinical applications.

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