Improving k-t SENSE by adaptive regularization.

The recently proposed method known as k-t sensitivity encoding (SENSE) has emerged as an effective means of improving imaging speed for several dynamic imaging applications. However, k-t SENSE uses temporally averaged data as a regularization term for image reconstruction. This may not only compromise temporal resolution, it may also make some of the temporal frequency components irrecoverable. To address that issue, we present a new method called spatiotemporal domain-based unaliasing employing sensitivity encoding and adaptive regularization (SPEAR). Specifically, SPEAR provides an improvement over k-t SENSE by generating adaptive regularization images. It also uses a variable-density (VD), sequentially interleaved k-t space sampling pattern with reference frames for data acquisition. Simulations based on experimental data were performed to compare SPEAR, k-t SENSE, and several other related methods, and the results showed that SPEAR can provide higher temporal resolution with significantly reduced image artifacts. Ungated 3D cardiac imaging experiments were also carried out to test the effectiveness of SPEAR, and real-time 3D short-axis images of the human heart were produced at 5.5 frames/s temporal resolution and 2.4 x 1.2 x 8 mm3 spatial resolution with eight slices. (c) 2007 Wiley-Liss, Inc.