Method and device for correcting magnetic resonance image, storage medium, and terminal

1 . A method for correcting magnetic resonance image, comprising:

acquiring an Apparent Diffusion Coefficient (ADC) image to be corrected, which is generated by scanning a target object using an echo planar imaging sequence from a pulse sequence of a portable mobile magnetic resonance apparatus and converting;

inputting the ADC image to be corrected into a pre-constructed image correction model to correct a grayscale value of tissue position of the ADC image to be corrected; wherein the pre-constructed image correction model is generated by function fitting based on magnetic resonance image sequences of different objects; and

outputting a target image corresponding to the ADC image to be corrected,

wherein before acquiring the ADC image to be corrected, the method further comprises:

acquiring a magnetic resonance image sequence of a first object, wherein the magnetic resonance image sequence comprises a first traditional magnetic resonance ADC image, a first mobile magnetic resonance ADC image and a first T1 image, and the image sequence of the first object is the image sequence of any object in an image dataset;

using the first mobile magnetic resonance ADC image of the first object as an image template to perform image registration on the first traditional magnetic resonance ADC image and the first T1 image respectively so as to obtain a second traditional magnetic resonance ADC image and a second T1 image of the first object;

segmenting and labelling structural tissue of the second T1 image of the first object to obtain an anatomical structure labelling map of the first object; and

obtaining the pre-constructed image correction model based on the second traditional magnetic resonance ADC image, the first mobile magnetic resonance ADC image and the anatomical structure labelling map of the first object.

2 . The method according to claim 1 , wherein the first traditional magnetic resonance ADC image is generated through a traditional fixed magnetic resonance apparatus, the first mobile magnetic resonance ADC image is generated through a portable mobile magnetic resonance apparatus, and the first T1 image is generated by scanning according to a target sequence from the pulse sequence of the traditional fixed magnetic resonance apparatus and converting.

3 . The method according to claim 1 , wherein the obtaining a pre-constructed image correction model based on the second traditional magnetic resonance ADC image, the first mobile magnetic resonance ADC image and the anatomical structure labelling map of the first object comprises:

loading a target parameter correction function predefined for the ADC image scanned and converted by the portable mobile magnetic resonance apparatus;

using image parameters of the second traditional magnetic resonance ADC image of the first object as a first variable of the target parameter correction function, using image parameters of the first mobile magnetic resonance ADC image as a second variable of the target parameter correction function, and using image parameters of the anatomical structure labelling map as a third variable of the target parameter correction function to obtain an image parameter expression of the first object; wherein the first variable is a corrected pADC image IC pADC , the second variable is a pADC image to be corrected I pADC , and the third variable is an anatomical structure segmentation class il;

performing function fitting based on the image parameter expression of the first object and the least square method to obtain an image correction function; and

modeling based on the image correction function to obtain a pre-constructed image correction model.

4 . The method according to claim 3 , wherein the target parameter correction function is:

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where IC pADC is the corrected pADC image, I pADC is the pADC image to be corrected, and il is the anatomical structure segmentation class; ix and iy are horizontal and vertical coordinates of each pixel position of the target tissue in the image respectively, N class is the number of anatomical structure segmentation class, and W, H are the width and height of the image respectively; x is the independent variable, i is the order of the polynomial function, a i is the coefficient of each order, and N f is the number of orders of the polynomial function.

5 . The method according to claim 1 , wherein the using the first mobile magnetic resonance ADC image of the first object as an image template to perform image registration on the first traditional magnetic resonance ADC image and the first T1 image respectively so as to obtain a second traditional magnetic resonance ADC image and a second T1 image of the first object comprises:

using the first mobile magnetic resonance ADC image of the first object as the image template;

extracting first feature points in the first traditional magnetic resonance ADC image and the image template, and matching the first feature points to obtain first matching feature points of the image template and the first traditional magnetic resonance ADC image;

extracting second feature points in the first T1 image and the image template, and matching the second feature points to obtain second matching feature points of the image template and the first T1 image;

generating a second traditional magnetic resonance ADC image of the first object based on the first matching feature points; and

generating a second T1 image of the first object based on the second matching feature points.

6 . The method according to claim 5 , wherein the generating a second traditional magnetic resonance ADC image of the first object based on the first matching feature points comprises:

using the first matching feature points as a constraint condition to deform the first traditional magnetic resonance ADC image so that the position of the deformed image is consistent with the position of the matching feature points of the image template, thus obtaining the second traditional magnetic resonance ADC image of the first object.

7 . A device for correcting magnetic resonance image, comprising:

an image acquisition module, which is configured to acquire an Apparent Diffusion Coefficient (ADC) image to be corrected, which is generated by scanning using an echo planar imaging sequence from a pulse sequence of a portable mobile magnetic resonance apparatus and converting;

an image parameter correction module, which is configured to input the ADC image to be corrected into a pre-constructed image correction model to correct a grayscale value of tissue position of the ADC image to be corrected; wherein the pre-constructed image correction model is generated by function fitting based on magnetic resonance image sequences of different objects; and

an image output module, which is configured to output a target image corresponding to the ADC image to be corrected;

wherein the device is configured to:

acquire a magnetic resonance image sequence of a first object, wherein the magnetic resonance image sequence comprises a first traditional magnetic resonance ADC image, a first mobile magnetic resonance ADC image and a first T1 image, and the image sequence of the first object is the image sequence of any object in an image dataset;

use the first mobile magnetic resonance ADC image of the first object as an image template to perform image registration on the first traditional magnetic resonance ADC image and the first T1 image respectively so as to obtain a second traditional magnetic resonance ADC image and a second T1 image of the first object;

segment and label structural tissue of the second T1 image of the first object to obtain an anatomical structure labelling map of the first object; and

obtain the pre-constructed image correction model based on the second traditional magnetic resonance ADC image, the first mobile magnetic resonance ADC image and the anatomical structure labelling map of the first object.

8 . A terminal, comprising a processor and a memory; wherein computer programs are stored in the memory, and the computer programs are suitable for being loaded by the processor to execute the method according to claim 1 .