Rapid magnetic resonance imaging method and system

Abstract

The invention relates to a rapid magnetic resonance imaging method which comprises the following steps of: carrying out variable-density sampling to obtain space data; carrying out sparse conversion to generate a measurement matrix; and carrying out signal reconstruction on the measurement matrix by a regularization self-adaption matching tracking algorithm. According to the rapid magnetic resonance imaging method and system, less space data is obtained through variable-density sampling, and high-resolution reconstruction of the image is completed under the action of the regularization self-adaption matching tracking algorithm; and due to the combination of the variable density sampling and the regularization self-adaption matching tracking algorithm, the imaging speed is greatly increased, the imaging quality is enhanced, and the imaging resolution in dynamic imaging is improved.

Description

【Technical field】 [0001] The invention relates to magnetic resonance technology, in particular to a fast magnetic resonance imaging method and system. 【Background technique】 [0002] With the development of magnetic resonance technology, magnetic resonance imaging has become an important means of clinical medical examination. It has the advantages of no radiation hazard, multi-directional and multi-parameter imaging, and is very sensitive to the examination of soft tissues. It can not only display the morphological information of human anatomical structures , and can also reflect some physiological information of human tissues. [0003] However, the imaging speed of magnetic resonance imaging is slow, and the physiological movement of the subject's body during the imaging process will blur the image, which cannot meet the dynamic imaging requirements of moving organs. In traditional fast imaging methods, such as compressed sensing imaging methods, a small amount of data col...

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Problems solved by technology

Find the minimum l 0 The norm problem requires an exhaustive enumeration of possible permutations in all sparsities of the nonzero values ​​in the signal and cannot be solved directly

And other methods for obtaining suboptimal solutions, for example, the minimum l 0 Norm method, greedy iterative matching pursuit algorithm, etc., in the minimum l 0 The linear programming method in the norm method can reconstruct the magnetic resonance image, but at the cost of high time, it is not suitable for dynamic imaging, and the greedy iterative matching pursuit algorithm can get the optimal solution quickly, but it is established On the basis of known sparsity, the sparsity of signals in practical applications is often unknown, and it is not suitable for high-resolution dynamic imaging

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