Multi-Contrast MRI Reconstruction via Information-Growth Holistic Unfolding Network

As an emerging technique, deep unfolding networks (DUNs) has drawn increasing attentions in the field of MRI reconstruction. With the integration of traditional iterative algorithms and modern learning-based blocks, DUNs enjoy extensive reputations of high interpretability and easy tractability. However, the performance of most DUNs can hardly reach a clinical level due to several limitations such as cross-stage independence, feature insufficiency, and interaction deficiency. To address these issues, we propose a novel information-growth holistic unfolding network, dubbed IHUN, which is stage-wisely armed with two main steps, i.e., a coarse estimation to guarantee data consistency and a feature calibration to suppress noises. More importantly, three collaborated subassemblies, including information-growth feature fusion block (I2FB), holistic feature extraction block (HFEB), and high-frequency cross feature fusion block (H3FB), are used in the feature calibration step to ensure a desirable information flow. Specifically, I2FB hierarchically aggregates the hidden states of previous stages to effectively update current solution. Operating in a computationally efficient fashion, HFEB takes charge of capturing the local/global features and boosting the inductive capacity. H3FB effectively fuses high-frequency information from two distinct modalities, thereby enabling the precise restoration of intricate details. Extensive experiments conducted on benchmark MRI datasets demonstrate the appealing performance of the proposed method, in terms of both quality and quantity. Codes are available at (
https://github.com/chenjiachengzzz/IHUN
).