Unsupervised Adaptive Implicit Neural Representation Learning for
  Scan-Specific MRI Reconstruction
Junwei Yang Pietro Liò
In recent studies on MRI reconstruction, advances have shown significant
promise for further accelerating the MRI acquisition. Most state-of-the-art
methods require a large amount of fully-sampled data to optimise reconstruction
models, which is impractical and expensive under certain clinical settings. On
the other hand, for unsupervised scan-specific reconstruction methods,
overfitting is likely to happen due to insufficient supervision, while
restrictions on acceleration rates and under-sampling patterns further limit
their applicability. To this end, we propose an unsupervised, adaptive
coarse-to-fine framework that enhances reconstruction quality without being
constrained by the sparsity levels or patterns in under-sampling. The framework
employs an implicit neural representation for scan-specific MRI reconstruction,
learning a mapping from multi-dimensional coordinates to their corresponding
signal intensities. Moreover, we integrate a novel learning strategy that
progressively refines the use of acquired k-space signals for self-supervision.
This approach effectively adjusts the proportion of supervising signals from
unevenly distributed information across different frequency bands, thus
mitigating the issue of overfitting while improving the overall reconstruction.
Comprehensive evaluation on a public dataset, including both 2D and 3D data,
has shown that our method outperforms current state-of-the-art scan-specific
MRI reconstruction techniques, for up to 8-fold under-sampling.