Variable Augmented Network for Invertible MR Coil Compression

TL;DR

This paper introduces VAN-ICC, a novel invertible neural network for coil compression in MRI that achieves higher compression ratios and flexibility, improving data storage and reconstruction speed without sacrificing image quality.

Contribution

The paper presents a variable augmentation network leveraging invertible models for high-precision, flexible coil compression in MRI, outperforming traditional methods.

Findings

VAN-ICC achieves higher compression ratios than traditional methods.

The method maintains performance across different numbers of virtual coils.

Experiments show improved image quality and reconstruction speed.

Abstract

A large number of coils are able to provide enhanced signal-to-noise ratio and improve imaging performance in parallel imaging. Nevertheless, the increasing growth of coil number simultaneously aggravates the drawbacks of data storage and reconstruction speed, especially in some iterative reconstructions. Coil compression addresses these issues by generating fewer virtual coils. In this work, a novel variable augmentation network for invertible coil compression termed VAN-ICC is presented. It utilizes inherent reversibility of normalizing flow-based models for high-precision compression and invertible recovery. By employing the variable augmentation technology to image/k-space variables from multi-coils, VAN-ICC trains invertible networks by finding an invertible and bijective function, which can map the original data to the compressed counterpart and vice versa. Experiments conducted on both fully-sampled and under-sampled data verified the effectiveness and flexibility of VAN-ICC. Quantitative and qualitative comparisons with traditional non-deep learning-based approaches demonstrated that VAN-ICC can carry much higher compression effects. Additionally, its performance is not susceptible to different number of virtual coils.