Method and system for dynamic magnetic resonance imaging

Abstract

The invention discloses a method and system for dynamic magnetic resonance imaging. A norm sparsity constraint is carried out on a spatial basis on the basis of a conventional PS (Partial Separability) method, so that image artifact and reconstruction noise are more effectively reduced, the image quality is greatly improved, and the reconstruction of a high temporal-spatial resolution dynamic magnetic resonance image is effectively realized. The method comprises the following steps: acquiring K-t spatial navigation data and dynamic image data, which are collected in a sampling mode based on a partial separability method; constructing a dynamic imaging reconstruction model containing a spatial basis function and a temporal basis function based on the partial separability method; acquiring the temporal basis function according to the navigation data; based on the dynamic imaging reconstruction model, estimating the spatial basis function by a least square method according to the temporal basis function and the dynamic image data; utilizing the temporal basis function and the dynamic image data to carry out interpolation recovery on the k spatial dynamic image data, and then carrying out Fourier transformation so as to acquire a reconstructed dynamic magnetic resonance image.

Description

technical field [0001] The present invention relates to dynamic magnetic resonance imaging technology, in particular to a dynamic magnetic resonance imaging method and system. Background technique [0002] The radiation-free, high soft-tissue contrast and multi-plane imaging based on magnetic resonance imaging make it one of the most popular clinical diagnostic tools. However, due to the limitations of Nyquist sampling theory (ie, Nyquist sampling theorem) and hardware systems, when acquiring dynamic MRI signals, only a limited part of K-space signals can often be acquired. For example, heart imaging, dynamic contrast-enhanced liver imaging, blood flow imaging, etc., when these limited collected data are directly used for Fourier reconstruction, large motion artifacts will be generated, so it is necessary to use dynamic magnetic resonance data in the The low-rank and sparse characteristics in the time dimension are restricted to recover unacquired data to achieve high-resol...

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Problems solved by technology

[0004] Although the partially separable function algorithm and compressed sensing theory can prove that MRI can achieve high-quality image reconstruction under the condition of sparse sampling, and break through the limitation of Nyquist sampling theorem in theory, there are still some problems in the clinical application of MRI. certain difficulty

Because the two sparse imaging models have different emphases, the partially separable function algorithm focuses on the sparsity of K-t spatial data, and the conventional partially separable function algorithm cannot accurately calculate the highly undersampled image signal, and the reconstructed image often loses Accompanied by certain noise; while the compressive sensing theory focuses on the sparsity of K-space data, and has many model parameters and low robustness.

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