Free-breathing cardiac MRI using bandlimited manifold modelling

TL;DR

This paper presents a new bandlimited manifold approach and algorithm for reconstructing free-breathing, ungated cardiac MRI images from highly undersampled data, enabling clinically comparable results without manual parameter tuning.

Contribution

It introduces a novel kernel low-rank algorithm to estimate the manifold structure and recover images, eliminating the need for breath-hold scans and manual parameter tuning.

Findings

Successful recovery of free-breathing cardiac MRI images

Qualitative similarity to breath-held scans

No manual parameter tuning required

Abstract

We introduce a novel bandlimited manifold framework and an algorithm to recover freebreathing and ungated cardiac MR images from highly undersampled measurements. The image frames in the free breathing and ungated dataset are assumed to be points on a bandlimited manifold. We introduce a novel kernel low-rank algorithm to estimate the manifold structure (Laplacian) from a navigator-based acquisition scheme. The structure of the manifold is then used to recover the images from highly undersampled measurements. A computationally efficient algorithm, which relies on the bandlimited approximation of the Laplacian matrix, is used to recover the images. The proposed scheme is demonstrated on several patients with different breathing patterns and cardiac rates, without requiring the need for manually tuning the reconstruction parameters in each case. The proposed scheme enabled the recovery of free-breathing and ungated data, providing reconstructions that are qualitatively similar to breath-held scans performed on the same patients. This shows the potential of the technique as a clinical protocol for free-breathing cardiac scans.