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A large number of coils are able to provide enhanced signal-to-noise ratioand improve imaging performance in parallel imaging. Nevertheless, theincreasing growth of coil number simultaneously aggravates the drawbacks ofdata storage and reconstruction speed, especially in some iterativereconstructions. Coil compression addresses these issues by generating fewervirtual coils. In this work, a novel variable augmentation network forinvertible coil compression termed VAN-ICC is presented. It utilizes inherentreversibility of normalizing flow-based models for high-precision compressionand invertible recovery. By employing the variable augmentation technology toimage/k-space variables from multi-coils, VAN-ICC trains invertible networks byfinding an invertible and bijective function, which can map the original datato the compressed counterpart and vice versa. Experiments conducted on bothfully-sampled and under-sampled data verified the effectiveness and flexibilityof VAN-ICC. Quantitative and qualitative comparisons with traditional non-deeplearning-based approaches demonstrated that VAN-ICC can carry much highercompression effects. Additionally, its performance is not susceptible todifferent number of virtual coils.

Variable Augmented Network for Invertible MR Coil Compression