  Clinical routine and retrospective cohorts commonly include multi-parametric
Magnetic Resonance Imaging; however, they are mostly acquired in different
anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints.
Thus acquired views suffer from poor out-of-plane resolution and affect
downstream volumetric image analysis that typically requires isotropic 3D
scans. Combining different views of multi-contrast scans into high-resolution
isotropic 3D scans is challenging due to the lack of a large training cohort,
which calls for a subject-specific framework. This work proposes a novel
solution to this problem leveraging Implicit Neural Representations (INR). Our
proposed INR jointly learns two different contrasts of complementary views in a
continuous spatial function and benefits from exchanging anatomical information
between them. Trained within minutes on a single commodity GPU, our model
provides realistic super-resolution across different pairs of contrasts in our
experiments with three datasets. Using Mutual Information (MI) as a metric, we
find that our model converges to an optimum MI amongst sequences, achieving
anatomically faithful reconstruction. Code is available at:
https://github.com/jqmcginnis/multi_contrast_inr/
