  Magnetic Resonance Imaging (MRI) is one of the most flexible and powerful
medical imaging modalities. This flexibility does however come at a cost; MRI
images acquired at different sites and with different parameters exhibit
significant differences in contrast and tissue appearance, resulting in
downstream issues when quantifying brain anatomy or the presence of pathology.
In this work, we propose to combine multiparametric MRI-based static-equation
sequence simulations with segmentation convolutional neural networks (CNN), to
make these networks robust to variations in acquisition parameters. Results
demonstrate that, when given both the image and their associated physics
acquisition parameters, CNNs can produce segmentations that exhibit robustness
to acquisition variations. We also show that the proposed physics-informed
methods can be used to bridge multi-centre and longitudinal imaging studies
where imaging acquisition varies across a site or in time.
