Method for training post-processing device for denoising MRI image and computing device for the same
Disclosed is a training method including outputting an MRI signal from a plurality of coils included in an MRI scanner and performing, by a computing device, supervised learning on a post-processing part included in the computing device by using, as training input data, a first image generated using a first group of coils among the plurality of coils and using, as a label, a second image generated using a second group of coils among the plurality of coils.
1 . A magnetic resonance imaging (MRI) system comprising:
an MRI scanner including a first group of coils and a second group of coils; and
a computing device including a post-processing part for post-processing an MRI image and a training management part,
wherein the computing device is configured to:
generate a first MRI image based on a first group of MRI signals obtained from the first group of coils, the first MRI image being used as training input data for supervised learning of the post-processing part, and
generating a label image based on a second group of MRI signals obtained from the second group of coils, the label image being used as a label for supervised learning of the post-processing part, and
wherein the training management part is configured to perform supervised learning on the post-processing part using the training input data and the label,
wherein the first MRI image and the label image are obtained through a same one-time data acquisition process performed by the MRI scanner.
2 . The MRI system of claim 1 ,
wherein generating the label image includes:
generating a second MRI image based on the second group of MRI signals;
generating an intermediate label image based on the second MRI image so as to eliminate a correlation between first noise in the first MRI image and second noise in the second MRI image; and
generating the label image based on the intermediate label image so as to compensate for a difference in sensitivity between the first group of coils and the second group of coils.
3 . The MRI system of claim 1 ,
wherein the first MRI image is an image obtained by synthesizing images of a first group generated from the MRI signals of the first group obtained from the first group of coils,
the label image is an image obtained by synthesizing images of a second group generated from the MRI signals of the second group obtained from the second group of coils, and
the MRI scanner includes a transform part configured to generate the images of the first group from the MRI signals of the first group and generate the images of the second group from the MRI signals of the second group.
4 . The MRI system of claim 2 , wherein the intermediate label image is generated based on a weighted sum of the first MRI image and the second MRI image.
5 . The MRI system of claim 1 ,
wherein generating the label image includes:
generating a second MRI image based on the second group of MRI signals; and
generating the label image based on the second MRI image so as to compensate for a difference in sensitivity between the first group of coils and the second group of coils.
6 . The MRI system of claim 1 ,
wherein while performing the supervised learning,
the post-processing part is configured to receive an input of the first MRI image to generate a post-processed image, and
the training management part is configured to train the post-processing part using a loss function between the post-processed image and the label image.
7 . A neural network training method for training a post-processing part configured to receive an input of a magnetic resonance imaging (MRI) image and denoise the MRI image, the method comprising:
generating, by an MRI scanner including a first group of coils and a second group of coils, a first MRI image based on a first group of MRI signals obtained from the first group of coils;
generating, by the MRI scanner, a label image based on a second group of MRI signals obtained from the second group of coils; and
performing, by a computing device, supervised learning on the post-processing part by using the first MRI image as training input data for supervised learning of the post-processing part and using the image as a label for supervised learning of the post-processing part,
wherein the first image and the label image are obtained through a same one-time data acquisition process performed by the MRI scanner.