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A Parallel Magnetic Resonance Imaging Method Based on Generative Adversarial Networks

A magnetic resonance imaging and magnetic resonance technology, applied in magnetic resonance measurement, measurement using nuclear magnetic resonance image system, biological neural network model, etc., can solve the problem of slow imaging speed and achieve the effect of avoiding the need for random sampling

Active Publication Date: 2021-04-30
ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY +1
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Problems solved by technology

[0007] In order to solve the problem of slow imaging speed due to the need for a large number of training samples in the deep learning-based magnetic resonance imaging method, the present invention provides a parallel magnetic resonance imaging method based on a generative confrontation network

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  • A Parallel Magnetic Resonance Imaging Method Based on Generative Adversarial Networks
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  • A Parallel Magnetic Resonance Imaging Method Based on Generative Adversarial Networks

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Embodiment Construction

[0035] The present invention will be further described below with reference to the embodiments, and the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, other used embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] combined with figure 1 and attached figure 2 , the parallel magnetic resonance imaging method based on generative adversarial network of the present invention, the method is completed by at least one processor, and the method includes:

[0037] Full sampling is performed on the middle region of the parallel magnetic resonance k-space data to obtain full-sampled data, and the surrounding regions of the parallel magnetic resonance k-space data are down-sampled at intervals to obtain down-sampled data; in some embodiments, the parallel magnetic reso...

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Abstract

The invention belongs to the technical field of magnetic resonance imaging, and discloses a parallel magnetic resonance imaging method based on a generative confrontation network, which is used to solve the problem that the existing parallel magnetic resonance imaging method based on deep learning requires a large number of training samples and causes slow imaging speed . The method of the present invention includes: performing full sampling on the middle area of ​​the parallel magnetic resonance K-space data to obtain full sampling data, down-sampling the surrounding areas of the parallel magnetic resonance K-space data to obtain the down-sampled data; using the generated network model to reconstruct the parallel magnetic resonance The unsampled data of the surrounding area of ​​the K-space data; the full-sampled data, the down-sampled data and the unsampled data are combined to form a complete parallel magnetic resonance K-space data; Spatial data is converted into images.

Description

technical field [0001] The invention belongs to the technical field of magnetic resonance imaging, in particular to a parallel magnetic resonance imaging method based on a generative confrontation network. Background technique [0002] Although Magnetic Resonance Imaging (MRI) has the advantages of no ionizing radiation, rich tissue contrast information, and non-invasive detection, the long scanning time limits its further promotion and application. [0003] Parallel magnetic resonance imaging technology can effectively reduce the time of magnetic resonance imaging while the scanning field of view and spatial resolution remain unchanged, so it has received extensive attention. However, affected by aliasing artifacts and noise amplification in the reconstruction process, the existing parallel MRI methods can only achieve good image quality with a very small sampling acceleration factor, and the imaging speed and reconstruction quality need to be further improved. improve. ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R33/54G01R33/56G01R33/561G06N3/04G06N3/08
CPCG01R33/54G01R33/5608G01R33/5611G06N3/08G06N3/045
Inventor 郑倩许林张世征邓璐娟张志锋宋胜利张建伟
Owner ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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