ArticleMagnetic resonance in medicine2021
Region-optimized virtual (ROVir) coils: Localization and/or suppression of spatial regions using sensor-domain beamforming.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 33 citations in OpenAlex.
- Fast Localized Calibration for Spatial-Spectral Excitation Without Fly-Back Gradients.Magnetic resonance in medicine · 2026Article
- Arc-ZTE: Continuously-Slewed Zero-TE Imaging With Incoherent Temporal Sampling for Near-Silent Dynamic MRI.Magnetic resonance in medicine · 2026Article
- K-space Optimized Spatial Temporal Architecture for Low Latency in Interventional Cardiac MRI.medRxiv : the preprint server for health sciences · 2026Article
- 3D MR Fingerprinting for Quantitative Bladder Wall TNMR in biomedicine · 2026Article
- Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion.Magnetic resonance in medicine · 2026Article
- An evaluation of brain volume and cortical thickness measurement at 0.55 T.Magma (New York, N.Y.) · 2026Article
- K-CC-MoCo: A Fast k-Space-Based Respiratory Motion Correction for Highly Accelerated First-Pass Perfusion Cardiovascular MR.Magnetic resonance in medicine · 2026Article
- MoCo + ROVir: Synergy Between Respiratory Motion Compensation and Cardiac Receive Region Focusing for Cardiac MRI.Magnetic resonance in medicine · 2026Article
- Feasibility Study for Dual Phase Free Breathing 3D bSSFP MRA With Preserved Eddy-Current Tolerance With Adaptive k-Space Lengths (PETAL) Acquisition.Magnetic resonance in medicine · 2026Article
- Navigator-gated free-breathing joint TMagma (New York, N.Y.) · 2026Article
- Prospective Respiratory Motion Correction Using Machine Learning and Pilot Tone (PROMPT) in Cardiac MRI.Magnetic resonance in medicine · 2026Article
- Efficient Interleaved Multi-Band Outer Volume Suppression for Highly Accelerated Simultaneous Multi-Slice Imaging of the Heart.Bioengineering (Basel, Switzerland) · 2026Article
- Contrast-optimized basis functions for self-navigated motion correction in quantitative MRI.Magnetic resonance in medicine · 2026Article
- Extending real-time MRI of the oral cavity using simultaneous multislice and compressed sensing.Magnetic resonance in medicine · 2026Article
- Spatiotemporal Maps for Dynamic MRI Reconstruction.IEEE transactions on computational imaging · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Multifrequency Time-Dependent Deep Image Prior for Real-Time Free-Breathing Cardiac Imaging.NMR in biomedicine · 2025Article
- Democratizing cardiac imaging with an automated magnetic resonance exam.Research square · 2025Article
- Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI.ArXiv · 2025Article
- Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.Magma (New York, N.Y.) · 2025Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeIn 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
methodsWe 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.
resultsROVir 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.
conclusionThe 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.
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