Respiratory Motion Correction of Compressively Sampled Myocardial Perfusion Data by Using Robust Matrix Decomposition

Abstract

Motion correction is a challenging problem in free breathing undersampled cardiac perfusion magnetic resonance images. It is due to aliasing artifacts in the reconstructed images and the rapid contrast changes in the perfusion images. In addition to the reconstruction limitations, many registration algorithms underperforms in the presence of the rapid intensity changes. In this paper, we propose a novel motion correction technique that reconstructs the motion-free images from the undersampled cardiac perfusion MR data. The technique utilizes the robust principal component analysis along with the periodic decomposition to separate the respiratory motion component that can be registered, from the unchanged contrast intensity variations. It was tested on synthetic data, simulated data, and the clinically acquired data. The performance of the method was qualitatively assessed and validated by comparing manually acquired time–intensity curves of the myocardial sectors to automatically generated curves before and after registration.