Apparatus and method for image restoration of accelerated MRI based on deep learning

A deep learning based accelerated MRI image quality restoring method, the method comprising:

• generating a low quality of second MRI data set for training by applying an MRI image accelerated scanning simulator to a first MRI data set for training;
• extracting test information from the second MRI data set for training and grouping the second MRI data set for training into a plurality of groups according to a predetermined rule;
• generating and training a plurality of deep learning models to be trained so as to correspond to each grouped second MRI data set for every group;
• extracting test information from an input accelerated MRI image;
• selecting at least one deep learning model corresponding to the test information, among a plurality of previously trained deep learning models;
• and
• outputting an MRI image with a restored image quality with respect to the input accelerated MRI image with the input accelerated MRI image as an input of at least one selected deep learning model,
• wherein in the selecting, the plurality of previously trained deep learning models is a plurality of deep learning models to be trained which is trained by the training, and
• wherein in the generating step, the MRI image accelerated scanning simulator performs:
• generating composite k-space data with an original image of the first MRI data set for training as an input;
• generating first low quality composite k-space data by applying a predetermined level of sub sampling to the composite k-space data;
• generating second low quality composite k-space data by adding a predetermined level of noise to the first low quality composite k-space data;
• generating a composite low quality MRI image based on the second low quality composite k-space data;
• and
• subtracting the original image from the generated composite low quality MRI image to generate a composite image quality degraded component MRI image.