Method and apparatus for generating subject-specific magnetic resonance angiography images from other multi-contrast magnetic resonance images

A computer-implemented method for synthesising magnetic resonance angiography (MRA) images from other types of inputted magnetic resonance (MR) images, in a subject-specific manner, the method comprising:

• providing a conditional generative adversarial network (cGAN) that learns a combined latent representation of the inputted magnetic resonance images for each subject and learns to transform this combined latent representation to a magnetic resonance angiography image corresponding to that subject;
• providing a plurality of magnetic resonance (MR) images as input into the cGAN;
• and
• outputting a plurality of MRA images from the cGAN based on the plurality of inputted MR images;
• wherein the cGAN comprises:
• a Generator network that learns a combined latent representation of the inputted MR images for each subject and learns to transform the latent representation to MRA images for each corresponding subject,
• a Discriminator network operating at a single or multiple spatial scales/resolution, to compete with the Generator network and improve the performance of the Generator network at synthesising MRA images using a loss function, and
• wherein the loss function comprises a plurality of 2D projection MRA domain-specific perceptual loss terms that act on maximum intensity projection images along three different axes:
• axial, coronal and sagittal.