METHODS AND SYSTEMS FOR MAGNETIC RESONANCE IMAGING

1 . A method implemented on a computing device including at least one processor and a storage device, the method, comprising:

obtaining magnetic resonance (MR) images of a subject;

obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model, a second count of the target MR mappings being less than a first count of the MR images;

wherein the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images.

2 . The method of claim 1 , wherein the obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model includes:

obtaining a relaxation time corresponding to at least one of the MR images; and

obtaining the one or more target MR mappings by processing the MR images and the relaxation time corresponding to the at least one of the MR images through the trained machine learning model.

3 . The method of claim 1 , wherein the first count is determined based on the second count.

4 . The method of claim 1 , further comprising:

determining the second count based on one or more target quantitative parameters of the subject, each of the one or more target MR mappings corresponding to one of the one or more target quantitative parameters.

5 . The method of claim 4 , wherein the MR images include at least one MR image, a type of a target imaging parameter corresponding to the at least one MR image being the same as a type of one of the one or more target quantitative parameters.

6 . The method of claim 1 , wherein one of the sub-models includes at least one of a fully connected (FC) network, a convolutional neural network (CNN), a recurrent neural network (RNN), or a Transformer.

7 . The method of claim 1 , wherein the trained machine learning model is obtained through operations including:

obtaining multiple training samples, wherein each training sample of the multiple training samples includes sample MR images and a reference mapping;

performing multiple iterations on a preliminary machine learning model based on the multiple training samples to obtain the trained machine learning model; the preliminary machine learning model including at least two sub-models.

8 . The method of claim 7 , wherein at least one iteration of the multiple iteration includes:

obtaining a predicted mapping by inputting the sample MR images into the preliminary machine learning model;

determining a value of a target loss function based on the predicted mapping and the reference mapping; and

updating network parameters of the at least two sub-models based on the value of the target loss function.

9 . The method of claim 7 , wherein the target loss function includes at least two loss terms, and each loss term corresponds to a sub-model.

10 . The method of claim 9 , wherein at least one of the at least two loss terms includes a weighting factor, the weighting factor being updated when updating network parameters of the at least two sub-models.

11 . The method of claim 1 , wherein the obtaining the one or more target mappings corresponding to at least a portion of the MR images by processing of the MR images through a trained machine learning network includes:

obtaining a first MR mapping corresponding to a target quantitative parameter by processing a first portion of the MR images through a first sub-model;

obtaining a second MR mapping corresponding to the target quantitative parameter by processing a second portion of the MR images through a second sub-model;

based on weight parameters of the first sub-model and the second sub-model, obtaining a target MR mapping corresponding to the target quantitative parameter by weighting the first MR mapping and the second MR mapping.

12 . The method of claim 1 , wherein the obtaining the one or more target mappings corresponding to at least a portion of the MR images by processing of the MR images through a trained machine learning network includes:

obtaining a first MR mapping corresponding to a target quantitative parameter by processing the MR images through a first sub-model;

obtaining a second MR mapping corresponding to the target quantitative parameter by processing the MR images through a second sub-model;

based on weight parameters of the first sub-model and the second sub-model, obtaining a target MR mapping corresponding to the target quantitative parameter by weighting the first MR mapping and the second MR mapping.

13 . The method of claim 1 , wherein the first count is equal to 2, and the MR images includes a first MR image corresponding to a first target imaging parameter and a second MR image corresponding to a second target imaging parameter, the one or more target MR mappings corresponding to a target quantitative parameter whose type is same as a type of the first target imaging parameter or the second target imaging parameter.

14 . The method of claim 1 , wherein the MR images are acquired in one single scan, and the one or more target MR mappings include at least one of a T1 mapping, a T2 mapping, or a T1rho mapping.

15 . The method of claim 1 , wherein the MR images are acquired in one single scan, and the one or more target MR mappings include one of a T1 mapping, a T2 mapping, and a T1rho mapping.

16 . The method of claim 1 , wherein obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model includes:

inputting T1-weighted MR images acquired in one single scan corresponding to each of the T1 weighted MR images into the trained machine learning model; and

generating a T1 mapping by the trained machine learning model.

17 . The method of claim 1 , wherein obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model includes:

inputting T2-weighted MR images acquired in one single scan corresponding to each of the T2-weighted MR images into the trained machine learning model; and

generating a T2 mapping by the trained machine learning model.

18 . The method of claim 1 , wherein obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model includes:

inputting T1rho-weighted MR images acquired in one single scan corresponding to each of the T1rho-weighted MR images into the trained machine learning model; and

generating a T1rho mapping by the trained machine learning model.

19 . A system for magnetic resonance imaging, comprising:

at least one processor and at least one storage, wherein

the at least one storage is configured to store computer instructions; and

the at least one processor is configured to execute at least a portion of the computer instructions to:

obtaining magnetic resonance (MR) images of a subject, at least two of the MR images being acquired by an MRI scanner according to different imaging parameters;

obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model, a second count of the target MR mappings being less than a first count of the MR images;

wherein the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images.

20 . A computer-readable storage medium storing computer instructions, wherein when reading the computer instructions in the storage medium, the computer performs a method including:

obtaining magnetic resonance (MR) images of a subject, at least two of the MR images being acquired by an MRI scanner according to different imaging parameters;

obtaining one or more target MR mappings corresponding to at least a portion of the MR images by processing the MR images through a trained machine learning model, a second count of the target MR mappings being less than a first count of the MR images;

wherein the trained machine learning model includes at least two sub-models, and each sub-model processes at least one of the MR images.