Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields
This addresses the challenge of limited data availability for cardiac MRI reconstruction, offering an incremental improvement over existing methods.
The authors tackled the problem of reconstructing accelerated cardiac cine MRI without needing large training datasets by proposing an unsupervised method based on neural fields, achieving good image quality and improved temporal depiction compared to a state-of-the-art technique at undersampling factors of 26x and 52x.
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.