Method and device for reconstructing magnetic resonance multi-contrast image

Abstract

The application discloses a method for reconstructing a magnetic resonance multi-contrast image. A final reconstructed image is obtained by adding the transition image and the residual image of an image to be corrected, wherein the image to be corrected is at least one of initial images of N contrasts, the transition image has lower noise and artifacts than the initial images and loses a part of organizational structure information, the lost part of the organizational structure information can be compensated by the organizational structure information in the residual image, and the final imageobtained by the addition of the transition image and the residual image can maintain the fidelity of the image. Since the transition image and the residual image do not change the contrast and the resolution of the original image and both have low noise and artifacts, the final image obtained by the addition of the transition image and the residual image can maintain the contrast and the resolution of the image, and also has low noise and artifacts. The application also discloses a device for a reconstructing a magnetic resonance multi-contrast image.

Description

technical field [0001] The present application relates to the field of medical imaging, in particular to a reconstruction method and device for magnetic resonance multi-contrast images. Background technique [0002] Magnetic resonance imaging is a multi-contrast imaging technique that can obtain multiple contrast images and provide rich information for diagnosis. [0003] For example, T1 and T2 weighted images can reflect the molecular biology and histological characteristics of the examined site to a certain extent, and provide information for the detection and diagnosis of diseases. Generally speaking, T1-weighted images can better display the growing anatomical structure, and T2-weighted images are more sensitive to the display of lesions. Diffusion Weighted Imaging (DWI) is based on the movement of water molecules, which can reflect information based on the physiological state of the brain and provide important information for the identification and diagnosis of intracr...

AI Technical Summary

Problems solved by technology

[0006] Existing parallel imaging technologies such as Sensitivity Encoding (SENSE) and Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) technology can effectively reduce the MRI scanning time, but inevitably reduce the The signal-to-noise ratio of the image and introduce certain artifacts in the image

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