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Improved reconstruction method for parallel magnetic resonance images

A magnetic resonance image, reconstruction technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve the problems of accuracy dependence on weight coefficients, deviation of reconstruction results, etc.

Active Publication Date: 2016-11-16
东台城东科技创业园管理有限公司
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Problems solved by technology

However, the fitting accuracy of the GRAPPA algorithm is too dependent on the weight coefficients, and the noise in the actual collected data will have a large deviation in the reconstruction results

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  • Improved reconstruction method for parallel magnetic resonance images
  • Improved reconstruction method for parallel magnetic resonance images
  • Improved reconstruction method for parallel magnetic resonance images

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Embodiment Construction

[0054] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0055] Such as figure 1 Shown, the concrete flowchart that the present invention realizes is as follows:

[0056] (1) The under-sampling scanning method is adopted in the phase encoding direction, and the full-sampling scanning method is adopted in the frequency encoding direction. The obtained K-space data is recorded as a data matrix D of P×Q×C dimension, where P represents the number of data lines in the phase encoding direction, and Q Indicates the number of data lines in the frequency encoding direction, and C indicates the number of scanning coils. The central area of ​​the K-space data is the calibration area, which is scanned by full sampling. If the dimension of the calibration area in the phase encoding direction is M, then the data matrix corresponding to this area Among them, the values ​​of P, M and Q are assumed to be even num...

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Abstract

The invention belongs to the field of magnetic resonance imaging, discloses an improved reconstruction method for parallel magnetic resonance images and particularly relates to a reconstruction method suitable for K spatial data in the parallel magnetic resonance imaging process. According to the method, when a weight coefficient is fitted, estimation is carried out with a K spatial data area and a self-calibration data area near undersampled data instead of adjacent points and a self-calibration line, and thus the nonlinear relation of K spatial data can be better fitted; noise in the sampled signals can be suppressed in the singular value decomposition and truncation processing process, and it is ensured that the signal-to-noise ratio of images is high. The method is a GRAPPA improving method based on matrix generalized inverse and singular value decomposition, the noise of the K spatial data can be suppressed, the fitting precision can be improved, and meanwhile the imaging quality can still be ensured when an acceleration factor is large.

Description

technical field [0001] The invention belongs to the field of magnetic resonance imaging, and relates to a parallel magnetic resonance imaging method, in particular to a reconstruction method suitable for K-space data in the parallel magnetic resonance imaging process. Background technique [0002] In recent decades, Magnetic Resonance Imaging (MRI) has been a hot topic in medical imaging and is widely used in clinical diagnosis and scientific research. Traditional magnetic resonance imaging performs full sampling of K-space data, the imaging speed is slow, and the imaging quality is easily affected by movement factors such as breathing and blood flow, resulting in artifacts. Aiming at the problems in traditional magnetic resonance imaging, many parallel magnetic resonance imaging techniques have been proposed. For example, in the literature: Griswold MA, Jakob PM, Heidemann RM, et al.Generalized autocalibrating partially parallel acquisition. Magn Reson Med.2002; 47:1202-12...

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Application Information

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IPC IPC(8): A61B5/055
CPCA61B5/055
Inventor 戴继生郑锐顾容榕滕涛
Owner 东台城东科技创业园管理有限公司
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