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Multiple-shot planar echo magnetic resonance imaging method based on neural network

A magnetic resonance imaging and neural network technology, which is applied in the measurement of magnetic variables, measurement devices, instruments, etc., can solve the problems of inability to reliably estimate phase changes, large phase errors, and unrealistic reconstruction time, and is easy to popularize and implement. , Accurate phase estimation, the effect of fast reconstruction

Active Publication Date: 2021-05-07
FUDAN UNIV
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Problems solved by technology

But in fact, these conventional model-based algorithms cannot reliably estimate the phase change, especially where the phase error is larger where the field is severely inhomogeneous, and various improvement methods are only limited to the improvement of image resolution, while Long reconstitution times further make these techniques impractical for clinical use

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  • Multiple-shot planar echo magnetic resonance imaging method based on neural network
  • Multiple-shot planar echo magnetic resonance imaging method based on neural network
  • Multiple-shot planar echo magnetic resonance imaging method based on neural network

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[0026] In the following, taking the application of DWI and DTI as an example, the specific implementation of the present invention will be described in detail in conjunction with the accompanying drawings. figure 1 The network structure diagram of the multiple excitation echo planar reconstruction method for the neural network, figure 2 A schematic flow chart of the method. It should be noted that on the premise of not departing from the concept of the present invention, several modifications and improvements to the following steps all belong to the protection scope of the present invention.

[0027] Step 1: Divide subjects into two groups, one group of subjects undergoes routine SSH-EPI DWI and SSH-EPI DTI scans in head scans.

[0028] Step 2: For the second group of subjects, MSH-EPI DWI and MSH-EPI DTI weighted magnetic resonance image sequences with 4 excitations except the conventional SSH-EPI DWI and SSH-EPI DTI in the head scan were performed. DWI contains 0-1000s / mm...

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Abstract

The invention belongs to the technical field of magnetic resonance imaging, and particularly relates to a multiple-shot plane echo magnetic resonance imaging method based on a deep learning neural network. The invention provides a brand-new method for accurate phase correction and rapid image reconstruction for multiple-shot plane echo magnetic resonance imaging. Compared with a traditional under-sampling image reconstruction algorithm based on a model, the deep learning neural network is utilized, deep learning is applied to aliasing correction of multiple shot echo planar imaging (MSH-EPI) images, and an aliasing-free single shot echo planar imaging (SSH-EPI) image is used as a target in the training stage. An aliasing SSH-EPI image having the same under-sampling factor and trajectory as the multiple shot plane echo is sent to the network so as to improve the precision of phase estimation.

Description

technical field [0001] The invention belongs to the technical field of magnetic resonance imaging, in particular to a multi-excitation planar echo magnetic resonance reconstruction method based on a deep learning neural network. Background technique [0002] Echo Planar Imaging (EPI) includes single-shot (Single-shot, SSH) and multiple-shot EPI (Multi-shot, MSH). Because of its high efficiency and insensitivity to motion, it is currently a diffusion-weighted Widely used acquisition sequences for Diffusion Weighted Imaging (DWI), Magnetic Resonance Imaging (MRI) and cardiac imaging. Based on Multi-shot Echo Planar Imaging (MSH-EPI) technology, it solves the problems of low spatial resolution, serious geometric distortion, blurred image and obvious artifacts of single echo planar imaging. It has very high application value in applications with high spatial resolution requirements such as high resolution diffusion weighting. By reducing the phase-encoding steps per excitation...

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Application Information

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IPC IPC(8): G01R33/565
CPCG01R33/565
Inventor 张会王鹤
Owner FUDAN UNIV
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