Compressed sensing magnetic resonance imaging method based on iterative p-threshold projection algorithm

Abstract

The invention discloses a compressed sensing magnetic resonance imaging method based on an iterative p-threshold projection algorithm, and belongs to the field of magnetic resonance imaging. The method comprises the following steps of: performing Fourier transform on a T2 weighted brain graph, transforming the T2 weighted brain graph subjected to Fourier transform into a K space to obtain K spacedata of the T2 weighted brain graph, and adopting the K space data of the T2 weighted brain graph according to a nonlinear sampling template to obtain T2 weighted brain graph under-sampling K space data; carrying out redundancy transformation on the T2 weighted brain graph undersampling k space data, and carrying out sparse representation on the T2 weighted brain graph undersampling k space data;and carrying out image reconstruction on the sparsely represented T2 weighted brain map undersampling k space data based on an iterative p-threshold projection algorithm or a fast iterative p-threshold projection algorithm, designing a new sparse target function by flexibly changing a p value, and achieving a better reconstruction effect .

Description

Technical field[0001]The invention relates to the field of magnetic resonance imaging, in particular to a compressed sensing magnetic resonance imaging method based on an iterative p-threshold projection algorithm.Background technique[0002]Magnetic resonance imaging (Magnetic Resonance Imaging, MRI) plays an important role in clinical diagnosis. Although MRI can provide high-quality images with excellent soft tissue contrast information, its imaging speed is not satisfactory and is mainly affected by physical (such as The amplitude and rate of change of the gradient pulse) and physiological (neural stimulation) constraints. The reconstruction model under the redundant system aims to recover the original data from the incomplete observation data using prior information. At present, related work is mainly based on two aspects: decomposition model and comprehensive model reconstruction. For the solution of comprehensive models, iterative soft-thresholding algorithms (ISTA) are widely u...

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Problems solved by technology

However, the threshold function used by the fast iterative soft-threshold projection algorithm has slow convergence speed and low precision, and cannot impose greater penalties on small coefficients, resulting in high final imaging errors; the proximal mapping of the penalty function can solve the regularization inverse problem, Therefore, the soft threshold function is very effective as the proximal mapping of the corresponding penalty function

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