Fast magnetic resonance imaging method and device based on deep convolution neural network

Abstract

The invention provides a fast magnetic resonance imaging method and device based on a deep convolution neural network. The fast magnetic resonance imaging method comprises steps of constructing a deep convolution neural network, obtaining an offline magnetic resonance image data, training the deep convolution neural network, learning a mapping relation between an undersampled magnetic resonance image and a full sampled image, using a deep convolution neural network learned through the S2 to reconstruct a resonance image. The fast magnetic resonance imaging method and device based on deep convolution neural network learn an offline deep convolution neural network through using substantial sampled magnetic resonance data, learns a mapping relation between the sampled magnetic resonance image and the full sampled image, fully uses offline substantial the magnetic resonance images to develop the prior information, enables the offline network to recover more fine structure and image characteristics from the undersampled resonance data and improves the magnetic resonance multiple and the imaging accuracy.

Description

technical field [0001] The invention relates to the technical field of magnetic resonance imaging, in particular to a fast magnetic resonance imaging method and device based on a deep convolutional neural network. Background technique [0002] The successful application of compressed sensing theory must meet the following three conditions: ① the signal is sparse, ② the artifacts caused by undersampling are incoherent in the transform domain, and ③ the reconstruction results have good consistency with the sampled data. In magnetic resonance images, these three conditions can be well met. In the classic fast magnetic resonance imaging model based on compressed sensing, there are usually two components: data fitting item and sparse regularization item. Suppose the reconstructed MRI image is m, ψ represents the sparse transformation from the pixel domain to the sparse domain, and F u Represents the undersampling operator of K space, y is the K space data measured in the scan, ...

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Problems solved by technology

[0005] However, most of the traditional fast MRI reconstruction methods are based on the compressed sensing framework, which only uses part of the acquired K-space data and develops image sparsity to constrain the imaging model for MRI image reconstruction, while a large number of offline MRI Data are underutilized and development of prior information remains limited

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