Multi-b-value DWI (diffusion weighted image) noise reduction method based on mutual information

A mutual information and image technology, applied in the field of image processing, can solve difficult problems such as data quality improvement

Active Publication Date: 2015-04-29
TIANJIN UNIV
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Problems solved by technology

However, studies have shown that the noise distribution of DWI images obeys the Rice distribution
Recently, some people have proposed weighted mean filter, non-local mean filter, linear minimum mean square error, etc., these methods can ef

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  • Multi-b-value DWI (diffusion weighted image) noise reduction method based on mutual information
  • Multi-b-value DWI (diffusion weighted image) noise reduction method based on mutual information
  • Multi-b-value DWI (diffusion weighted image) noise reduction method based on mutual information

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[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the implementation manners of the present invention will be further described in detail below.

[0022] Using the non-local mean filtering method based on mutual information, the similarity of image information between different channels can be fully utilized, and the advantages of non-local mean filtering can be used to effectively remove noise while retaining image edge information. Therefore, this method can be used for DWI images with multiple b-values ​​and multiple directions. In addition, different b-value images have different signal-to-noise ratios, so the parameters of the noise reduction method based on mutual information should be set separately according to different b-value images. The b0 image refers to the image obtained without adding diffusion-sensitive gradient pulses. Because of its high signal-to-noise ratio, contrast and signal strength, its noise dist...

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Abstract

The invention discloses a multi-b-value DWI (diffusion weighted image) noise reduction method based on mutual information. The method comprises steps as follows: acquiring to-be-tested multi-b-value DWIs in multiple diffusion sensitive gradient directions by a magnetic resonance scanner; performing preprocessing such as head movement correction, eddy current correction and the like on the images; estimating different b-value image signal-to-noise ratios; performing noise reduction processing on the images through setting parameters of an optimal noise reduction algorithm so as to acquire DWIs with higher signal-to-noise ratios. The method fully considers features of different images, and three-dimensional structural features of the processed images are reserved as many as possible while noises are eliminated. During establishment of a high-order complex model, different b images have higher image quality, so that acquired high-order parameter images can have smaller errors and fewer singular values. The method can be better applied to preprocessing of a high-order model imaging technology and plays an active role in further research on white matter fiber structures.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a mutual information-based noise reduction method for multi-b value diffusion weighted images. Background technique [0002] Diffusion Weighted Image (DWI) is a new magnetic resonance (MR) imaging technique developed on the basis of nuclear magnetic resonance. It uses the diffusion motion characteristics of water molecules in living tissue for imaging, and can form diffusion-weighted images by adding diffusion-sensitive gradients to conventional MR sequences. Due to the complex electromagnetic environment in the imaging process, various noises and artifacts will appear in DWI, including artifacts related to the main magnetic field, artifacts related to the radio frequency magnetic field, eddy current noise, etc., and movement will occur due to factors such as head movement Artifacts. The existence of these noise factors will directly affect the identification of tissue structure...

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Application Information

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IPC IPC(8): G06T5/00A61B5/055
Inventor 赵欣王伟伟陈元园沙淼杨佳佳明东
Owner TIANJIN UNIV
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