Group‐Patch Joint Compression: Compressing Dynamic <scp> B <sub>0</sub> </scp> and Static <scp>RF</scp> Spatial Modulations Across k‐Space Subregion Groups for Highly Accelerated <scp>MRI</scp>

ABSTRACT Purpose To accelerate MRI further, rapid B 0 field modulations can be applied during oversampled readout to capture additional physical information, as in Wave‐CAIPI/FRONSAC/local B 0 coils modulation techniques. These methods, however, turn the Fourier readout into a non‐Fourier‐encoded dimension that cannot be reconstructed by FFT, posing significant reconstruction challenges especially in compressed‐sensing or neural‐network frameworks. Theory and Methods Because the rapid B 0 modulations still vary slowly relative to the oversampled ADC dwell time, we exploit this encoding redundancy by compressing k‐space patch‐by‐patch across subregions, each of which is jointly encoded by a distinct subset of B 0 and RF (receive) spatial encoding functions. For each subset, a compression matrix is computed once and reused to compress all patches encoded by the same B 0 ‐RF spatial modulations. This can be implemented by feeding subsets of B 0 and RF spatial encoding maps into an adapted conventional RF array compression algorithm, mimicking an expanded set of virtual receiver channels. This approach was evaluated on human brain scans at 9.4 T/3 T. Results The proposed group‐patch joint compression achieves substantially higher compression factors than conventional RF‐only compression, while minimally compromising encoding efficiency. Typically, joint compression factors of 11×–20× led to negligible encoding loss, dramatically reducing reconstruction time and peak memory usage. For example, compressed‐sensing reconstruction took 1.4–5.1 s/2D slice, 177 s–10.1 min/3D volume on a high‐memory CPU node. Conclusion Given joint encoding of dynamic B 0 and static RF fields, compressing multidimensional k‐space patches in separate groups outperforms compressing RF receivers alone. This substantially mitigates a fundamental computational bottleneck when combining rapid B 0 and RF‐receiver modulations.

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Publication Details

Journal
Magnetic Resonance in Medicine
Published
2026-07-23
DOI
https://doi.org/10.1002/mrm.70439
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Group‐Patch Joint Compression: Compressing Dynamic B 0 and Static RF Spatial Modulations Across k‐Space Subregion Groups for Highly Accelerated MRI

Rui Tian, Klaus Scheffler
Magnetic Resonance in Medicine
Advanced MRI Techniques and Applications
article

Group‐Patch Joint Compression: Compressing Dynamic B 0 and Static RF Spatial Modulations Across k‐Space Subregion Groups for Highly Accelerated MRI

Rui Tian, Klaus Scheffler
article en

Abstract

ABSTRACT Purpose To accelerate MRI further, rapid B 0 field modulations can be applied during oversampled readout to capture additional physical information, as in Wave‐CAIPI/FRONSAC/local B 0 coils modulation techniques. These methods, however, turn the Fourier readout into a non‐Fourier‐encoded dimension that cannot be reconstructed by FFT, posing significant reconstruction challenges especially in compressed‐sensing or neural‐network frameworks. Theory and Methods Because the rapid B 0 modulations still vary slowly relative to the oversampled ADC dwell time, we exploit this encoding redundancy by compressing k‐space patch‐by‐patch across subregions, each of which is jointly encoded by a distinct subset of B 0 and RF (receive) spatial encoding functions. For each subset, a compression matrix is computed once and reused to compress all patches encoded by the same B 0 ‐RF spatial modulations. This can be implemented by feeding subsets of B 0 and RF spatial encoding maps into an adapted conventional RF array compression algorithm, mimicking an expanded set of virtual receiver channels. This approach was evaluated on human brain scans at 9.4 T/3 T. Results The proposed group‐patch joint compression achieves substantially higher compression factors than conventional RF‐only compression, while minimally compromising encoding efficiency. Typically, joint compression factors of 11×–20× led to negligible encoding loss, dramatically reducing reconstruction time and peak memory usage. For example, compressed‐sensing reconstruction took 1.4–5.1 s/2D slice, 177 s–10.1 min/3D volume on a high‐memory CPU node. Conclusion Given joint encoding of dynamic B 0 and static RF fields, compressing multidimensional k‐space patches in separate groups outperforms compressing RF receivers alone. This substantially mitigates a fundamental computational bottleneck when combining rapid B 0 and RF‐receiver modulations.

Magnetic Resonance in Medicine
Max Planck Institute for Biological Cybernetics (DE), University of Tübingen (DE)
European Research Council
Openalex Percentile: Top 9%
Advanced MRI Techniques and Applications
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