  Cardiac cine MRI is the gold standard for cardiac functional assessment, but
the inherently slow acquisition process creates the necessity of reconstruction
approaches for accelerated undersampled acquisitions. Several regularization
approaches that exploit spatial-temporal redundancy have been proposed to
reconstruct undersampled cardiac cine MRI. More recently, methods based on
supervised deep learning have been also proposed to further accelerate
acquisition and reconstruction. However, these techniques rely on usually large
dataset for training, which are not always available. In this work, we propose
an unsupervised approach based on implicit neural field representations for
cardiac cine MRI (so called NF-cMRI). The proposed method was evaluated in
in-vivo undersampled golden-angle radial multi-coil acquisitions for
undersampling factors of 26x and 52x, achieving good image quality, and
comparable spatial and improved temporal depiction than a state-of-the-art
reconstruction technique.
