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

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 the deblurring processing of cardiac nuclear 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, computed tomography CT, magnetoencephalography MEG, three-dimensional ultrasound imaging, solution positron emission tomography PET, single photon emission computed tomography SPECT, diffusion weighted imaging DWI, functional magnetic resonance FMRI, etc., these imaging techniques have their own characteristics, and they can provide people with various anatomical and functional information at different temporal and spatial resolutions. However, relying solely on the information provided by these devices is far from meeting people's requirements, and the image...

AI Technical Summary

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

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