Distortion correction of low field magnetic resonance images with paired high field magnetic resonance images using machine learning

A training system for a generative adversarial network, the training system comprising:

• a training processor;
• and
• a training memory storing a plurality of sets of training images, wherein each set of training images comprises a high-resolution training image of a training object of interest and a paired low-field strength MRI training image of the training object of interest, and wherein the memory stores instructions executable by the processor to:
• obtain a first set of training images, wherein the first set of training images comprises a first low-field strength MRI training image and a first high-resolution training image of a first training object of interest;
• input the first low-field strength MRI training image into a generator model of a generative adversarial network to generate a first distortion-corrected training image;
• input the first distortion-corrected training image and the first high-resolution training image into a discriminator model of the generative adversarial network;
• evaluate, by the discriminator model, the first distortion-corrected training image to identify the first distortion-corrected training image as one of “real” or “fake”;
• and
• update, preoperatively, the generative adversarial network based on the evaluation of the first distortion-corrected training image by the discriminator model, wherein updating the generative adversarial network comprises:
• if the discriminator model classified the first distortion-corrected training image from the generator model as “real”, updating the discriminator model;
• and
• if the discriminator model classified the first distortion-corrected training image from the generator model as “fake”, updating the generator model.

A method, comprising:

• training, preoperatively, a generative adversarial network to a desired performance level, wherein training the generative adversarial network comprises:
• inputting a first set of training images into the generative adversarial network, wherein the first set of training images comprises a first low-field strength MRI training image and a first high-resolution training image of a first training object of interest, and wherein the generative adversarial network comprises a generator model and a discriminator model;
• generating, by the generator model, a first distortion-corrected training image based on the first low-field strength MRI training image;
• receiving, by the discriminator model, the first distortion-corrected training image and the first high-resolution training image;
• classifying, by the discriminator model, the first distortion-corrected training image as “real” or “fake”;
• and
• updating the generative adversarial network based on the classification, wherein updating the generative adversarial network comprises:
• updating the discriminator model if the first distortion-corrected training image was classified as “real”;
• and
• updating the generator model if the first distortion-corrected training image was classified a “fake”;
• the method further comprising:
• transmitting a notification to a user interface based on the generative adversarial network reaching the desired performance level.