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MOTION DETERMINATION FOR VOLUMETRIC MAGNETIC RESONANCE IMAGING USING A DEEP MACHINE-LEARNING MODEL
MOTION DETERMINATION FOR VOLUMETRIC MAGNETIC RESONANCE IMAGING USING A DEEP MACHINE-LEARNING MODEL
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机译:用深层机器学习模型确定体积磁共振成像的运动
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摘要
For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.
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