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Reinforcement Learning for Online Sampling Trajectory Optimization for Magnetic Resonance Imaging
专利权人:
The Board of Trustees of the Leland Stanford Junior University
发明人:
David Y. Zeng,Shreyas S. Vasanawala,Joseph Yitan Cheng
申请号:
US16654368
公开号:
US20200134887A1
申请日:
2019.10.16
申请国别(地区):
US
年份:
2020
代理人:
摘要:
A magnetic resonance imaging scan performs an MRI acquisition using an undersampling pattern to produce undersampled k-space data; adds the undersampled k-space data to aggregate undersampled k-space data for the scan; reconstructs an image from the aggregate undersampled k-space data; updates the undersampling pattern from the reconstructed image and aggregate undersampled k-space data using a deep reinforcement learning technique defined by an environment, reward, and agent, where the environment comprises an MRI reconstruction technique, where the reward comprises an image quality metric, and where the agent comprises a deep convolutional neural network and fully connected layers; and repeats these steps to produce a final reconstructed MRI image for the scan.
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