Magnetic resonance image reconstruction method based on high-pass filtering

Abstract

The invention discloses a magnetic resonance image reconstruction method based on high-pass filtering. The method comprises the following steps of 1, conducting high-pass filtering on original k-spacedata; 2, downsampling the k-space data which is subjected to high-pass filtering, and using the k-space data which is subjected to high-pass filtering for sensitivity estimation to obtain a sensitivity graph; 3, regarding the downsampled k-space data and the sensitivity graph as input of a SENSE algorithm, and reconstructing a magnetic resonance image; 4, conducting two-dimension Fourier transformation on the reconstructed image, and mapping the reconstructed image to the k-space to obtain the corresponding k-space data; 5, conducting inverse high-passing filtering on the k-space data; 6, conducting inverse two-dimension Fourier transformation on the k-space data which is subjected to inverse high-pass filtering to obtain the final magnetic resonance image. By means of the magnetic resonance image reconstruction method based on high-pass filtering, the problem that a traditional SENSOR reconstructed magnetic resonance image is poor in image quality during high-rate downsampling is solved, the calculation efficiency is high, and the application range is wide.

Description

technical field [0001] The invention relates to the field of magnetic resonance image reconstruction, in particular to a high-pass filter-based magnetic resonance image reconstruction method. Background technique [0002] Magnetic resonance imaging of soft tissue has high resolution, multiple imaging parameters, and no ionizing radiation. It is currently a routine clinical examination method. A major disadvantage of magnetic resonance imaging is the long data acquisition time, which results in slow imaging. The emergence of multi-channel acquisition technology and parallel imaging algorithms has greatly accelerated the speed of magnetic resonance imaging. Parallel imaging methods commonly used in clinic include SENSE (sensitivity encoding), GRAPPA (generalized autocalibratingpartially parallel acquisitions) and so on. SENSE is currently the most widely used parallel imaging method in the image domain in clinical practice. As the acceleration factor increases, errors in coi...

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Problems solved by technology

[0003] In view of this, the present invention provides a method for reconstructing magnetic resonance images based on high-pass filtering for the problem of poor quality of magnetic resonance image reconstruction by SENSE in the case of high-magnification acceleration sampling in the above-mentioned prior art

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