  Deep neural networks for medical image reconstruction are traditionally
trained using high-quality ground-truth images as training targets. Recent work
on Noise2Noise (N2N) has shown the potential of using multiple noisy
measurements of the same object as an alternative to having a ground-truth.
However, existing N2N-based methods are not suitable for learning from the
measurements of an object undergoing nonrigid deformation. This paper addresses
this issue by proposing the deformation-compensated learning (DeCoLearn) method
for training deep reconstruction networks by compensating for object
deformations. A key component of DeCoLearn is a deep registration module, which
is jointly trained with the deep reconstruction network without any
ground-truth supervision. We validate DeCoLearn on both simulated and
experimentally collected magnetic resonance imaging (MRI) data and show that it
significantly improves imaging quality.
