Free-breathing cardiac MRI using bandlimited manifold modelling
This work addresses the challenge of motion artifacts in cardiac MRI for clinical applications, offering a potential protocol for free-breathing scans, though it appears incremental as it builds on existing manifold modeling approaches.
The authors tackled the problem of reconstructing free-breathing and ungated cardiac MRI images from highly undersampled measurements by introducing a bandlimited manifold framework and a kernel low-rank algorithm, achieving reconstructions qualitatively similar to breath-held scans without manual parameter tuning across multiple patients.
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.