Enhancing CS-MRI Reconstruction Using Improved ESSGAN with Convolutional Block Attention Module

Motivation: Inspired by DR-CAM-GAN's progress in CS-MRI, we embraced ESSGAN with self-attention mechanisms. Goal(s): To assess CBAM's impact on ESSGAN's ability to enhance CS-MRI reconstruction across diverse sampling rates. Approach: Implemented ESSGAN+CBAM and performed experiments using T1-weighted brain images from the MICCAI 2023 dataset. Ablation studies compared DR-CAM-GAN, ESSGAN, ESSGAN+CAM, and ESSGAN+CBAM across varying sampling rates. Results: At a 10% low sampling rate, ESSGAN and ESSGAN+CBAM demonstrated similar performance. Nevertheless, at higher sampling rates (≥20%), ESSGAN+CBAM outperformed all other models, affirming its effectiveness across evaluation metrics. Impact: The study reveals that the integration of CBAM modules significantly enhances ESSGAN's performance in CS-MRI, particularly at higher undersampling rates, making it a valuable tool for rapid and accurate image reconstruction in clinical settings.