  In this work, we propose a novel image reconstruction framework that directly
learns a neural implicit representation in k-space for ECG-triggered
non-Cartesian Cardiac Magnetic Resonance Imaging (CMR). While existing methods
bin acquired data from neighboring time points to reconstruct one phase of the
cardiac motion, our framework allows for a continuous, binning-free, and
subject-specific k-space representation.We assign a unique coordinate that
consists of time, coil index, and frequency domain location to each sampled
k-space point. We then learn the subject-specific mapping from these unique
coordinates to k-space intensities using a multi-layer perceptron with
frequency domain regularization. During inference, we obtain a complete k-space
for Cartesian coordinates and an arbitrary temporal resolution. A simple
inverse Fourier transform recovers the image, eliminating the need for density
compensation and costly non-uniform Fourier transforms for non-Cartesian data.
This novel imaging framework was tested on 42 radially sampled datasets from 6
subjects. The proposed method outperforms other techniques qualitatively and
quantitatively using data from four and one heartbeat(s) and 30 cardiac phases.
Our results for one heartbeat reconstruction of 50 cardiac phases show improved
artifact removal and spatio-temporal resolution, leveraging the potential for
real-time CMR.
