Deep learning and data self consistency-based magnetic resonance diffusion weighted imaging method

A diffusion-weighted imaging and deep learning technology, applied in image data processing, 2D image generation, medical science, etc., can solve problems such as low imaging resolution, achieve good image quality, improve performance, and improve network learning capabilities.

Active Publication Date: 2018-09-28
朱高杰
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Problems solved by technology

[0007] The purpose of the present invention is: the present invention discloses a magnetic resonance diffusion weighted imaging method based on deep learning and data self-consistency, which solves the problem of low imaging resolution caused by the limited parallel imaging performance factors of traditional diffusion weighted imaging

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  • Deep learning and data self consistency-based magnetic resonance diffusion weighted imaging method
  • Deep learning and data self consistency-based magnetic resonance diffusion weighted imaging method
  • Deep learning and data self consistency-based magnetic resonance diffusion weighted imaging method

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Embodiment 1

[0030] Step 1 includes the following steps:

[0031] Step 1.1: The network modules composed of data self-consistent layer, CNN network and phase-constrained layer are superimposed in sequence to complete the initial network construction. In each network module, the data self-consistent layer and the CNN network are connected many-to-many, and the CNN network and the phase-constrained layer are multiple one-to-one connection;

[0032] Step 1.2: Acquire the pre-scan data in the multi-shot diffusion weighted sequence, and generate the correction matrix and data self-consistent equation based on the scan data, such as figure 2 101-102 in;

[0033] Step 1.3: Calculate the sensitivity distribution of all receiving coils based on the rectification matrix as figure 2 In 104, the convolution kernel is calculated based on the data self-consistent equation such as figure 2 103 marked in;

[0034] Step 1.4: Acquire the navigator echo signal and imaging signal in the multi-shot diff...

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Abstract

The invention discloses a deep learning and data self consistency-based magnetic resonance diffusion weighted imaging method, and relates to the field of magnetic resonance diffusion weighted imaging.The method comprises the steps of 1: constructing a network by repeated superposition of a network module consisting of a data self consistency layer, a CNN and a phase restriction layer, and executing a sequence comprising pre-scanning and navigation echoes to determine input data and training labeled data of the network; 2: taking the training labeled data as a target, inputting an image corresponding to the input data to the constructed network, and obtaining an input output mapping relationship of the network through back propagation training; and 3: executing a sequence comprising pre-scanning and no navigation echoes to obtain an imaging signal and a data self consistency convolution kernel, inputting the imaging signal and the data self consistency convolution kernel to the network, and obtaining an output image through forward propagation mapping, thereby finishing the reconstruction. The problem of low imaging resolution of existing conventional diffusion weighted imaging dueto limitation of factors is solved, so that the effects of improving the network learning capability and increasing the imaging resolution are achieved.

Description

technical field [0001] The invention relates to the field of magnetic resonance diffusion weighted imaging, in particular to a magnetic resonance diffusion weighted imaging method based on deep learning and data self-consistency. Background technique [0002] Magnetic resonance diffusion imaging technology is a new technology that relies on the random movement of water molecules in the body to provide image contrast. The diffusion of water molecules in tissues conforms to a random thermal motion model, and the magnitude and direction of diffusion are affected by biofilms and biomacromolecules in tissues. When the gradient magnetic field exists, the diffusion motion of water molecules will cause the dephasing of the magnetization vector, resulting in the decrease of the magnetic resonance signal. The degree of MR signal reduction depends on the tissue type, structure, physical and physiological state and microenvironment. In the above process, the gradient magnetic field sp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00A61B5/055
CPCA61B5/055G06T11/005G06T11/008
Inventor 朱高杰
Owner 朱高杰
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