Abstract
A novel technique called βkβt GRAPPAβ is introduced for the acceleration of dynamic magnetic resonance imaging. Dynamic magnetic resonance images have significant signal correlations in kβspace and time dimension. Hence, it is feasible to acquire only a reduced amount of data and recover the missing portion afterward. Generalized autocalibrating partially parallel acquisitions (GRAPPA), as an important parallel imaging technique, linearly interpolates the missing data in kβspace. In this work, it is shown that the idea of GRAPPA can also be applied in kβt space to take advantage of the correlations and interpolate the missing data in kβt space. For this method, no training data, filters, additional parameters, or sensitivity maps are necessary, and it is applicable for either single or multiple receiver coils. The signal correlation is locally derived from the acquired data. In this work, the kβt GRAPPA technique is compared with our implementation of GRAPPA, TGRAPPA, and sliding window reconstructions, as described in Methods. The experimental results manifest that kβt GRAPPA generates high spatial resolution reconstruction without significant loss of temporal resolution when the reduction factor is as high as 4. When the reduction factor becomes higher, there might be a noticeable loss of temporal resolution since kβt GRAPPA uses temporal interpolation. Images reconstructed using kβt GRAPPA have less residue/folding artifacts than those reconstructed by sliding window, much less noise than those reconstructed by GRAPPA, and wider temporal bandwidth than those reconstructed by GRAPPA with residual kβspace. kβt GRAPPA is applicable to a wide range of dynamic imaging applications and is not limited to imaging parts with quasiβperiodic motion. Since only local information is used for reconstruction, kβt GRAPPA is also preferred for applications requiring real time reconstruction, such as monitoring interventional MRI. Magn Reson Med, 2005. Β© 2005 WileyβLiss, Inc.