Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI

Sahin Olut, Yusuf Huseyin Sahin, Ugur Demir, Gozde Unal

Abstract:

Magnetic Resonance Angiography (MRA) has become an essential MR contrast for
imaging and evaluation of vascular anatomy and related diseases. MRA acquisitions
are typically ordered for vascular interventions, whereas in typical scenarios, MRA
sequences can be absent in the patient scans. This motivates the need for a technique
that generates inexistent MRA from existing MR multi-contrast, which could be
a valuable tool in retrospective subject evaluations and imaging studies. In this
paper, we present a generative adversarial network (GAN) based technique to
generate MRA from T1-weighted and T2-weighted MRI images, for the first time
to our knowledge. To better model the representation of vessels which the MRA
inherently highlights, we design a loss term dedicated to a faithful reproduction of
vascularities. To that end, we incorporate steerable filter responses of the generated
and reference images inside a Huber function loss term. Extending the well-
established generator-discriminator architecture based on the recent PatchGAN
model with the addition of steerable filter loss, the proposed steerable GAN (sGAN)
method is evaluated on the large public database IXI. Experimental results show
that the sGAN outperforms the baseline GAN method in terms of an overlap score
with similar PSNR values, while it leads to improved visual perceptual quality.

Author Affiliation: Istanbul Technical University

Keywords: image synthesis, mr angiography, mra, gan, generative adversarial networks, steerable filters, image-to-image translation