A generalized approach to parallel magnetic resonance imaging

Abstract

Parallel magnetic resonance (MR) imaging uses spatial encoding from multiple radiofrequency detector coils to supplement the encoding supplied by magnetic field gradients, and thereby to accelerate MR image acquisitions beyond previous limits. A generalized formulation for parallel MR imaging is derived, demonstrating the relationship between existing techniques such as SMASH and SENSE, and suggesting new algorithms with improved performance. Hybrid approaches combining features of both SMASH-like and SENSE-like image reconstructions are constructed, and numerical conditioning techniques are described which can improve the practical robustness of parallel image reconstructions. Incorporation of numerical conditioning directly into parallel reconstructions using the generalized approach also removes a cumbersome and potentially error-prone sensitivity calibration step involving division of two distinct in vivo reference images. Hybrid approaches in combination with numerical conditioning are shown to extend the range of accelerations over which high-quality parallel images may be obtained.