  Multiple Sclerosis (MS) is a chronic neurological condition characterized by
the development of lesions in the white matter of the brain. T2-fluid
attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI)
provides superior visualization and characterization of MS lesions, relative to
other MRI modalities. Follow-up brain FLAIR MRI in MS provides helpful
information for clinicians towards monitoring disease progression. In this
study, we propose a novel modification to generative adversarial networks
(GANs) to predict future lesion-specific FLAIR MRI for MS at fixed time
intervals. We use supervised guided attention and dilated convolutions in the
discriminator, which supports making an informed prediction of whether the
generated images are real or not based on attention to the lesion area, which
in turn has potential to help improve the generator to predict the lesion area
of future examinations more accurately. We compared our method to several
baselines and one state-of-art CF-SAGAN model [1]. In conclusion, our results
indicate that the proposed method achieves higher accuracy and reduces the
standard deviation of the prediction errors in the lesion area compared with
other models with similar overall performance.
