A new approach to auto-calibrated dynamic parallel imaging based on the Karhunen-Loeve transform: KL-TSENSE and KL-TGRAPPA

TSENSE and TGRAPPA are auto-calibrated parallel imaging techniques that can improve the temporal resolution and/or spatial resolution needed in dynamic magnetic resonance imaging applications. In its original form, TSENSE uses temporal low-pass filtering of the under-sampled frames to create the sensitivity map. TGRAPPA uses a sliding-window moving average when finding the auto-calibrating signals. Both filtering methods are suboptimal in the least-squares sense, and may give rise to mismatches between the under-sampled 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 auto-calibration signals for TGRAPPA. Phantom experiments show that the new reconstruction method has comparable SNR 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