Heart magnetic resonance imaging method based on multi-scale low-rank model

Abstract

The invention discloses a heart magnetic resonance imaging method based on a multi-scale low-rank model, and provides a new method for heart magnetic resonance imaging researching. A radial sampling track is adopted to realize undersampling of heart K space data, and full heart data radial undersampling is realized, and thereafter magnetic resonance data acquisition speed is accelerated, and then scanning time of a magnetic resonance device is reduced. The minimization expression of the heart magnetic resonance data based on multi-scale low-rank decomposition is used to improve the precision of the magnetic resonance imaging. A convex optimization problem of a reconstructed image is solved by an alternating direction multiplier method, and a speed of magnetic resonance imaging reconstruction is improved, and therefore the texture of the result of the reconstructed image is clear, and the edge is smooth.

Description

technical field [0001] The invention belongs to the technical field of magnetic resonance imaging, and in particular relates to a cardiac magnetic resonance imaging method based on a multi-scale low-rank model. Background technique [0002] Cardiovascular disease is the leading cause of sudden cardiac arrest. At present, the incidence and mortality of cardiovascular diseases remain high, which increases the burden of cardiovascular disease prevention and treatment and becomes an important public health problem. It is urgent to strengthen the prevention and treatment of cardiovascular diseases. Cardiac Magnetic Resonance Imaging (CMR) is a technology that uses the principle of nuclear magnetic resonance to perform tomographic imaging of the human heart. It can accurately reflect the anatomical structure, morphological function, blood flow characteristics and myocardial activity of the heart. The main tool in diagnosis. Cardiac magnetic resonance imaging has good soft tissue...

AI Technical Summary

Problems solved by technology

[0004] At present, there are two main methods of MRI image reconstruction: one is multi-coil parallel imaging technology, which mainly uses the spatial sensitivity difference of a single receiving coil in the phased array coil to encode spatial information and reduce the gradient necessary for imaging. The number of encoding steps, which uses a multi-coil array to simultaneously collect signals, allows under-sampling of K space to reduce the number of phase encoding steps, while maintaining the same spatial resolution of the image, it can greatly shorten the scanning time and improve the imaging speed; but Multi-coil parallel imaging technology involves the estimation of multi-coil sensitivity distribution, which needs to increase the amount of calculation

[0005] Another MRI reconstruction method based on compressive sensing theory. Due to the low rank and sparseness of magnetic resonance images, compressive sensing theory can be used to reconstruct images from randomly undersampled k-space data, reducing sampling data and improving For imaging speed, low-rank transformation methods such as k-t SLR (k-t Sparsity and Low-Rank) and L+S (Low Rank plus Sparse) are currently commonly used. Although these methods take into account the low-rank characteristics, they do not consider the multi-scale of magnetic resonance images. low-rank features

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