Region‐optimized virtual (ROVir) coils: Localization and/or suppression of spatial regions using sensor‐domain beamforming
Daeun Kim, Stephen F. Cauley, Krishna S. Nayak, Richard M. Leahy, Justin P. Haldar
Abstract
Purpose
In many MRI scenarios, magnetization is often excited from spatial regions that are not of immediate interest. Excitation of uninteresting magnetization can complicate the design of efficient imaging methods, leading to either artifacts or acquisitions that are longer than necessary. While there are many hardware‐ and sequence‐based approaches for suppressing unwanted magnetization, this paper approaches this longstanding problem from a different and complementary angle, using beamforming to suppress signals from unwanted regions without modifying the acquisition hardware or pulse sequence. Unlike existing beamforming approaches, we use a spatially invariant sensor‐domain approach that can be applied directly to raw data to facilitate image reconstruction.
Theory and Methods
We use beamforming to linearly mix a set of original coils into a set of region‐optimized virtual (ROVir) coils. ROVir coils optimize a signal‐to‐interference ratio metric, are easily calculated using simple generalized eigenvalue decomposition methods, and provide coil compression.
Results
ROVir coils were compared against existing coil‐compression methods, and were demonstrated to have substantially better signal suppression capabilities. In addition, examples were provided in a variety of different application contexts (including brain MRI, vocal tract MRI, and cardiac MRI; accelerated Cartesian and non‐Cartesian imaging; and outer volume suppression) that demonstrate the strong potential of this kind of approach.
Conclusion
The beamforming‐based ROVir framework is simple to implement, has promising capabilities to suppress unwanted MRI signal, and is compatible with and complementary to existing signal suppression methods. We believe that this general approach could prove useful across a wide range of different MRI applications.