Region‐optimized virtual (ROVir) coils: Localization and/or suppression of spatial regions using sensor‐domain beamforming

Abstract

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 signalto-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. K E Y W O R D S accelerated acquisition, beamforming, multichannel MRI, reduced field-of-view imaging, signal suppression 198 | KIM et al. F I G U R E 1 Illustration of applications where the ROI (marked in green) can be much smaller than the full FOV. In functional MRI of the visual cortex, we may only care about the signal from the visual cortex itself, but not the signal from other parts of the brain. In speech imaging, we may only care about the signal from the vocal tract, but not the signal from the brain, scalp, neck, or torso. In cardiac imaging, we may only care about the MRI signal from the heart, but not the signal from other parts of the body. In MR spectroscopic imaging (MRSI), we may only care about the signal from brain parenchyma, but not the signal from extracranial lipids

Single breath‐hold CINE imaging with combined simultaneous multislice and region‐optimized virtual coils

“…In some subjects, residual aliasing is still seen outside the target cardiac ROI. This is entirely expected from ROVir, and could be easily mitigated by appropriate masking to the signal ROI as described in the previous ROVir paper 7 . We have not done such masking in this article for the sake of full transparency.…”

“…In previous retrospective analyses, ROVir has been shown to achieve substantial suppression of unwanted spatial regions and enable reduced FOV imaging in applications like brain, vocal tract and cardiac imaging. 7 In this work, we perform prospective experiments to investigate the feasibility of using ROVir to suppress unwanted signal from the torso in cardiac CINE imaging, which allows the use of a substantially smaller FOV than in conventional cardiac CINE. This represents the first prospective application of ROVir in a practical application.…”

Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction

“…Baron et al 17 derived the binary image from the eigenvalues of the estimated ESPIRiT 40 maps, which is not used in this work as the target TGAS-SPI-MRF application utilizes 48 receive coils during the acquisition, making ESPIRiT computationally expensive. Additionally, the FOV shifting is performed before applying any coil compression techniques, which enables the shifted GRE image to be used as a reference for further suppression of signals originating outside the FOV using the ROVir method 42 as outlined below.…”

“…The large FOV pre-scan was also used to pre-calculate a coil compression matrix, such that any signal originating from outside the TGAS-SPI-MRF FOV after shifting (e.g. shoulders) could be removed using region-optimized virtual coil compression 42 (ROVir) estimated from the GRE with the interference region set to any area outside the target FOV for the TGAS-SPI-MRF acquisition.…”

“…For the TGAS-SPI-MRF brain imaging application, signal from the shoulders and neck are particularly troublesome. This is overcome by utilizing the automatic FOV shifting approach proposed in Baron et al 17 that is augmented with a newly proposed coil compression method for additional robustness 42 .…”