  Supervised Deep-Learning (DL)-based reconstruction algorithms have shown
state-of-the-art results for highly-undersampled dynamic Magnetic Resonance
Imaging (MRI) reconstruction. However, the requirement of excessive
high-quality ground-truth data hinders their applications due to the
generalization problem. Recently, Implicit Neural Representation (INR) has
appeared as a powerful DL-based tool for solving the inverse problem by
characterizing the attributes of a signal as a continuous function of
corresponding coordinates in an unsupervised manner. In this work, we proposed
an INR-based method to improve dynamic MRI reconstruction from highly
undersampled k-space data, which only takes spatiotemporal coordinates as
inputs. Specifically, the proposed INR represents the dynamic MRI images as an
implicit function and encodes them into neural networks. The weights of the
network are learned from sparsely-acquired (k, t)-space data itself only,
without external training datasets or prior images. Benefiting from the strong
implicit continuity regularization of INR together with explicit regularization
for low-rankness and sparsity, our proposed method outperforms the compared
scan-specific methods at various acceleration factors. E.g., experiments on
retrospective cardiac cine datasets show an improvement of 5.5 ~ 7.1 dB in PSNR
for extremely high accelerations (up to 41.6-fold). The high-quality and inner
continuity of the images provided by INR has great potential to further improve
the spatiotemporal resolution of dynamic MRI, without the need of any training
data.
