Abstract

Background: 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 and might introduce biases. Methods: In this work we propose NF-cMRI, an unsupervised approach based on implicit neural field representations for cardiac cine MRI. We evaluate our method in in-vivo undersampled golden-angle radial multi-coil acquisitions for undersampling factors of 13x, 17x and 26x. Results: The proposed method achieves excellent scores in sharpness and robustness to artifacts and comparable or improved spatial–temporal depiction than state-of-the-art conventional and unsupervised deep learning reconstruction techniques. Conclusions: We have demonstrated NF-cMRI potential for cardiac cine MRI reconstruction with highly undersampled data.