Group-Patch Joint Compression: Compressing Dynamic B₀ and Static RF Spatial Modulations Across k-Space Subregion...

To accelerate MRI further, rapid B₀ field modulations can be applied during oversampled readout to capture additional physical information, as in Wave-CAIPI/FRONSAC/local B₀ 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.

Because the rapid B₀ 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 B₀ 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 B₀-RF spatial modulations. This can be implemented by feeding subsets of B₀ 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.

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.

Given joint encoding of dynamic B₀ 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 B₀ and RF-receiver modulations.