Automatic coil selection for channel reduction in SENSE-based parallel imaging

Coil arrays with large number of receive elements allow improved imaging performance and higher signal-to-noise-ratio. The MR systems supporting these arrays have to handle an increased amount of data and higher reconstruction burden. To overcome these problems, data reduction techniques need to be applied, realized either by linear combination of the original coil data prior to reconstruction or by discarding particular data from unimportant coil elements.

This work focuses on the latter approach and presents an efficient algorithm for automatic coil selection applicable to SENSE imaging. A singular value decomposition (SVD)-based coil selection is proposed that performs a coil element ranking quantifying the contribution of each coil element to the image reconstruction allowing appropriate coil selection. This approach makes use of the coil sensitivity information and takes reduction factor and phase encoding direction into account.

Simulations, phantom and in vivo experiments were performed to validate the SVD-based coil selection algorithm. The proposed approach proved to be computationally efficient without remarkable image quality degradation.

The SVD-based approach offers the opportunity for fast automatic coil selection. This could simplify clinical workflow and may, furthermore, pave the way for various 2D real-time and interventional applications.