# Free-breathing and ungated cardiac cine using navigator-less spiral SToRM

## TL;DR

This paper presents a novel kernel low-rank algorithm for free-breathing, ungated cardiac MRI reconstruction from spiral data without navigators, outperforming classical methods in quality and quantitative metrics.

## Contribution

It introduces a kernel low-rank matrix completion approach that directly recovers missing k-space data without explicit motion navigation, addressing challenges of motion-induced low-rankness.

## Key findings

- Improved image quality over classical low-rank and XD-GRASP methods.
- Quantitative metrics comparable to breath-held cine data.
- Validated with simulated and in-vivo data.

## Abstract

We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from undersampled measurements; extensive cardiac and respiratory motion often results in the Casorati matrix not being sufficiently low-rank. Therefore, we exploit the non-linear structure of the dynamic data, which gives the low-rank kernel matrix. Unlike prior work that rely on navigators to estimate the manifold structure, we propose a kernel low-rank matrix completion method to directly fill in the missing k-space data from variable density spiral acquisitions. We validate the proposed scheme using simulated data and in-vivo data. Our results show that the proposed scheme provides improved reconstructions compared to the classical methods such as low-rank and XD-GRASP. The comparison with breath-held cine data shows that the quantitative metrics agree, whereas the image quality is marginally lower.