Heart magnetic resonance imaging (MRI) image deblurring method based on sparse low rank and dictionary learning

A sparse image and dictionary learning technology, applied in the field of image processing, can solve the problems of reducing the image deblurring effect, ringing effect, artifacts, etc., and achieve the effects of avoiding image distortion, improving accuracy, and good adaptability

Active Publication Date: 2015-07-15
XIDIAN UNIV
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Problems solved by technology

Due to the beating of the heart and the flow of blood, weak borders, artifacts, and local gradient maximum regions appear in the MRI images, which seriously affect the quality of cardiac MRI images.
Because the spatial sampling of the image and the uncertainty of the image blur kernel will lead to the ringing effect, and the larger the size of the blur kernel, the more serious the corresponding ringing effect, the traditional cardiac MRI image deblurring method, often because the image The influence of noise, the inaccuracy of blur kernel estimation and the influence of factors such as blur kernel zero value defect make the result of deconvolution unsatisfactory, and there are often serious ringing effects in the restored image, which greatly reduces the image deconvolution. blur effect

Method used

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  • Heart magnetic resonance imaging (MRI) image deblurring method based on sparse low rank and dictionary learning
  • Heart magnetic resonance imaging (MRI) image deblurring method based on sparse low rank and dictionary learning
  • Heart magnetic resonance imaging (MRI) image deblurring method based on sparse low rank and dictionary learning

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Embodiment Construction

[0024] The method of the present invention is used to decompose a frame of cardiac magnetic resonance imaging MRI image, which is derived from one frame of the cardiac magnetic resonance imaging MRI sequence image of the same person.

[0025] Reference figure 1 The specific steps of using the method of the present invention to perform low-rank decomposition and deblurring on a frame of cardiac MRI image are as follows:

[0026] Step 1: Input the i-th frame image I in the cardiac MRI sequence image i , The cardiac magnetic resonance imaging MRI sequence includes 20 frames of images, each with a size of 192×160, here we take the first frame of the cardiac magnetic resonance imaging MRI sequence, such as figure 2 Shown

[0027] Step 2: Use alternate iteration method to MRI image of cardiac MRI I i Perform sparse low-rank matrix decomposition to obtain sparse image A and low-rank image B;

[0028] 2a) Set C to be the cardiac MRI image I i , Through the formula: A k + 1 = ...

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Abstract

The invention discloses a heart magnetic resonance imaging (MRI) image deblurring method based on sparse low rank and dictionary learning. The heart MRI image deblurring method mainly solves the problem that beating of a heart causes the quality reduction of a heart MRI image. The realization process of the heart MRI image deblurring method includes the steps: inputting a heart MRI image; carrying out sparse low rank matrix decomposition on the heart MRI image, and obtaining a sparse part and a low rank part of the image; selecting a sub window from the sparse part of the image; estimating motion blur kernel in the sub window through a method of self-adaption dictionary learning; carrying out deconvolution operation on the heart MRI image through utilization of the estimated motion blur kernel, and obtaining a clear heart MRI image. The heart MRI image deblurring method has the advantages of accurately estimating the motion blur kernel, and the distortion of image deblurring results due to inaccuracy of estimation of the motion blur kernel is avoided.

Description

Technical field [0001] The invention belongs to the technical field of image processing, and relates to the processing of medical images, in particular to a deblurring processing of cardiac magnetic resonance imaging MRI images. Background technique [0002] With the rapid development of medical imaging technology, a large number of high-resolution images have emerged, such as magnetic resonance imaging MRI, computer tomography CT, magnetoencephalography MEG, three-dimensional ultrasound imaging, positron emission tomography PET, single photon emission computer tomography SPECT, diffuse weighted imaging DWI, functional magnetic resonance FMRI, etc., these imaging technologies have their own characteristics, and they can provide people with various anatomical and functional information at different temporal and spatial resolutions. However, relying only on the information provided by these devices is far from meeting people's requirements, and image processing methods must be used...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 缑水平刘芳王越越唐晓焦李成王爽杨淑媛马文萍马晶晶
Owner XIDIAN UNIV
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