Motion correction in cardiac perfusion data by using robust matrix decomposition

TL;DR

This paper introduces a robust matrix decomposition approach for motion correction in cardiac perfusion MRI, effectively separating respiratory motion from contrast changes to improve image reconstruction quality.

Contribution

It presents a novel motion correction method combining robust PCA and periodic decomposition, addressing limitations of existing registration algorithms under rapid intensity changes.

Findings

Effective separation of respiratory motion from contrast variations.

Improved image quality in simulated and clinical data.

Better alignment of myocardial time-intensity curves.

Abstract

Motion free reconstruction of compressively sampled cardiac perfusion MR images is a challenging problem. It is due to the aliasing artifacts and the rapid contrast changes in the reconstructed perfusion images. In addition to the reconstruction limitations, many registration algorithms under perform in the presence of the rapid intensity changes. In this paper, we propose a novel motion correction method that reconstructs the motion free image series from the undersampled cardiac perfusion MR data. The motion correction method uses the novel robust principal component analysis based reconstruction along with the periodic decomposition to separate the respiratory motion component that can be registered, from the contrast intensity variations. It is tested on simulated data and the clinically acquired data. The performance of the method is qualitatively assessed and compared with the existing motion correction methods. The proposed method is validated by comparing manually acquired time-intensity curves of the myocardial sectors to automatically generated curves before and after registration.