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Rapid magnetic resonance imaging (MRI) method based on CS ( compressed sensing ) technique

A magnetic resonance imaging and compressed sensing technology, which is applied in the measurement of magnetic variables, measurement devices, instruments, etc., to achieve the effect of shortening the data acquisition time

Inactive Publication Date: 2011-02-16
HANGZHOU DIANZI UNIV
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[0009] The invention patents applied for above show that there is no patent specifically for MRI fast imaging compression sensing technology

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  • Rapid magnetic resonance imaging (MRI) method based on CS ( compressed sensing ) technique
  • Rapid magnetic resonance imaging (MRI) method based on CS ( compressed sensing ) technique
  • Rapid magnetic resonance imaging (MRI) method based on CS ( compressed sensing ) technique

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with accompanying drawing.

[0048] A fast MRI method based on CS compressive sensing technology consists of three steps: variable density stochastic k-space data acquisition, sparse transformation of MRI images, and non-linear optimized image reconstruction based on L1 norm minimization.

[0049] Variable density random k-space data acquisition includes four steps: determining the undersampling rate, generating the trajectory of variable density random data acquisition, collecting data and gridding:

[0050] 1-1 Determine the undersampling rate, according to the structural sparsity of the image, determine the undersampling rate, that is, the total amount of data points to be collected.

[0051] 1-2 Generate variable density random data acquisition trajectory, according to the undersampling rate in step 1-1, combined with the energy distribution law of k-space, densely collect in the central area of ​​...

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Abstract

The invention discloses a rapid magnetic resonance imaging method based on a CS (compressed sensing) technique. The traditional imaging method has relatively low speed and high hardware cost. The method comprises the following steps of: firstly, acquiring variable-density random k spatial data; specifically, determining under sampling rate according to the structural sparsity of an image; carrying out sparse acquisition in a k-space central area and carrying out random sparse acquisition in a k-space peripheral area according to the under sampling rate by combining with the k-space energy distribution rule to generate a variable-density random data acquisition path; acquiring the data according to the determined data acquisition path; then carrying out sparse conversion on an MRI image; and finally, nonlinearly optimizing and reconstructing the image based on Li norm minimum. The method breaks through the limit of the classical Nyguist sampling theorem, accurately reconstructs the signal of the MRI image through randomly acquiring few data points by utilizing a nonlinear optimization algorithm and greatly shortens the data acquisition time.

Description

technical field [0001] The invention belongs to the field of magnetic resonance imaging, and relates to a fast magnetic resonance imaging method based on compressed sensing technology. Background technique [0002] MRI (Magnetic Resonance Imaging) is a tomographic imaging technique that uses nuclear magnetic resonance signals measured in vitro to generate physical and chemical properties of internal organs. Because MRI has no adverse effects on the body and has the advantages of high soft tissue resolution ability, it has been widely used in clinical disease detection. However, the clinical application of MRI is often limited by the long data acquisition time, which limits the examination of some patients and affects the comfort of the patient when undergoing examination. The long-time examination of each patient increases the economic burden of the patient's MRI examination, restricts the utilization rate of expensive MRI equipment in the hospital, and also affects the eco...

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Application Information

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IPC IPC(8): G01R33/561
Inventor 金朝阳杜一平薛安克徐平陈华杰彭冬亮赵晓东
Owner HANGZHOU DIANZI UNIV
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