Synthetic method of magnetic resonance multi-channel image

Abstract

The invention discloses a synthetic method of a magnetic resonance multi-channel image. The method includes the following steps: (1) obtaining multi-channel K space data; (2) comparing the maximum value of a module of K space data of each channel to obtain CHmin which is the number of a channel where the minimum maximum value is located; (3) conducting inverse Fourier transform on the K space data of each channel to obtain an original image of the channel; (4) enabling the image of each channel to an index phase of the image of the CHmin channel to obtain images with a phase portion only comprising coil sensitivity information; (5) conducting lowpass filtering processing on all images; (6) conducting principal component analysis on smoothened image data to obtain optimal matching filter vector; (7) conducting weighting and summation of the original image of each channel by using a matching filter to obtain a final synthetic image. The synthetic method can guarantee that the synthetic image is close to optimal signal-to-noise ratio, enables signal strength of the image to have unbiasedness, and can eliminate signal loss caused by phase oscillation.

Description

technical field [0001] The invention belongs to the technical field of magnetic resonance imaging, and in particular relates to a method for synthesizing a magnetic resonance multi-channel image, which is used for obtaining an image with an optimal signal-to-noise ratio. Background technique [0002] Due to the advantages of high resolution and no radiation damage, magnetic resonance has been widely used in clinical and engineering fields. With the application of phased array technology to magnetic resonance imaging, imaging time and image signal-to-noise ratio have been optimized. The quality of the multi-channel image synthesis method is directly related to the quality of the final composite image. [0003] The sensitivity of phased array surface coils is localized. Only the tissue close to the coil has a high signal-to-noise ratio, and the signal-to-noise ratio drops sharply in the area far away from the coil. An image with uniform intensity is obtained by synthesizing mu...

AI Technical Summary

Problems solved by technology

Although it has the advantage of being simple and easy to implement, there are some problems, such as the general bias of signal strength, the significant enhancement of noise in non-signal parts, etc.

[0006] David et al published an adaptive matched filter synthesis method on the 43rd volume 682-690 of the Magnetic Resonance Journal in Medicine in 2000, and obtained a synthetic image with a signal-to-noise ratio close to the optimum. Moreover, the imaging time and local image quality can be changed through parameter adjustment, but for some special cases, such as the noise of a certain channel is very low, or the noise of each channel is at different levels, the noise suppression effect will be obvious. reduce

In 2002 Bader et al published a multi-channel synthesis method based on image domain coil sensitivity estimation in the Journal of Magnetic Resonance in Medicine, Vol. 47, pp. 539-548, by low-pass filtering each channel image Obtain almost noise-free sensitivity distribution images, and then synthesize them with a method similar to the sum of squares. They can obtain synthetic images with close to optimal signal-to-noise ratios, and have unbiased signal strength. However, when the subject organizes a certain The sharp change of magnetic susceptibility in some areas will cause image phase oscillation, the low-pass filter will filter out some useful signals, and the signal intensity of the corresponding area of ​​the final composite image will decrease

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