A new approach to autocalibrated dynamic parallel imaging based on the Karhunen‐Loeve transform: KL‐TSENSE and KL‐TGRAPPA

Abstract

TSENSE and TGRAPPA are autocalibrated parallel imaging techniques that can improve the temporal resolution and/or spatial resolution in dynamic magnetic resonance imaging applications. In its original form, TSENSE uses temporal low-pass filtering of the undersampled frames to create the sensitivity map. TGRAPPA uses a sliding-window moving average when finding the autocalibrating signals. Both filtering methods are suboptimal in the least-squares sense and may give rise to mismatches between the undersampled k-space raw data and the corresponding coil sensitivities. Such mismatches may result in aliasing artifacts when imaging patients with heavy breathing, as in real-time imaging of wall motion by MRI following a treadmill exercise stress test. In this study, we demonstrate the use of an optimal linear filter, i.e., the Karhunen-Loeve transform filter, to estimate the channel sensitivity for TSENSE and acquire the autocalibration signals for TGRAPPA. Phantom experiments show that the new reconstruction method has comparable signal-to-noise ratio performance to traditional TSENSE/TGRAPPA reconstruction. In vivo real-time cardiac cine experiments performed in five healthy volunteers post-exercise during rapid respiration show that the new method significantly reduces the chest wall aliasing artifacts caused by respiratory motion (P < 0.001).