  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.
