Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction

TL;DR

This paper introduces a novel, parameter-free, bio-inspired attention mechanism for cardiac MRI reconstruction, leveraging ecological principles to improve model performance without added complexity.

Contribution

It proposes a non-linear ecological difference equation-based attention module that is theoretically grounded and outperforms existing parameter-free attention methods.

Findings

Achieves superior MRI reconstruction quality compared to existing methods.

Demonstrates the effectiveness of ecological principles in designing neural network modules.

Provides a theoretically motivated, parameter-free attention mechanism for medical imaging.

Abstract

Attention is a fundamental component of the human visual recognition system. The inclusion of attention in a convolutional neural network amplifies relevant visual features and suppresses the less important ones. Integrating attention mechanisms into convolutional neural networks enhances model performance and interpretability. Spatial and channel attention mechanisms have shown significant advantages across many downstream tasks in medical imaging. While existing attention modules have proven to be effective, their design often lacks a robust theoretical underpinning. In this study, we address this gap by proposing a non-linear attention architecture for cardiac MRI reconstruction and hypothesize that insights from ecological principles can guide the development of effective and efficient attention mechanisms. Specifically, we investigate a non-linear ecological difference equation that describes single-species population growth to devise a parameter-free attention module surpassing current state-of-the-art parameter-free methods.