Fast magnetic resonance imaging method based on residual U-net convolutional neural network

Abstract

The invention discloses a fast magnetic resonance imaging method based on residual U-net convolutional neural network and the method comprises three steps of preparing training data, carrying out training based on the residual U-net convolutional neural network, and carrying out image reconstruction based on the residual U-net convolutional neural network. By adding the residual module into the U-net convolutional neural network, the problems of gradient disappearance, overfitting, low convergence speed and the like of the U-net convolutional neural network can be solved, and the quality of rapid MRI imaging based on the U-net convolutional neural network is improved.

Description

technical field

[0001] The invention belongs to the field of magnetic resonance imaging, and relates to a fast magnetic resonance imaging method based on a residual U-net convolutional neural network. Background technique

[0002] In the past 20 years, Magnetic Resonance Imaging (MRI) has developed rapidly due to its high soft tissue resolution and no ionizing radiation damage to the human body. However, due to the slow imaging speed of MRI, the physiological movement of the subject during the imaging process often causes imaging artifacts, which makes it difficult to meet the requirements of real-time imaging. Therefore, how to speed up the imaging speed of MRI is one of the hot spots in the research of MRI theory and technology. one.

[0003] Researchers often shorten the data acquisition time of MRI from three aspects. One is to improve the performance of MRI hardware and enhance the main magnetic field strength and gradient switching speed of the MRI scanner. The magne...

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