Benchmarking Deep Learning Approaches for Single-Excitation Wideband MRI: Establishing the First SE-Wideband Brain Dataset

Single-Excitation Wideband MRI enables efficient acquisition, but its rapid-switching design to mitigate intrinsic blur leads to incompatibility with GRAPPA and other fast sequences, restricting its clinical adoption. Regarding this challenge, we established the first dedicated SE-Wideband brain dataset, comprising 76 subjects and 8000 paired slices, and developed a systematic framework for benchmarking deep learning–based restoration methods. Nine networks were evaluated across multiple loss configurations, with performance quantified using conventional metrics and human perceptual ratings. Results demonstrate consistent improvements over raw SE-Wideband inputs, improving NRMSE from ~ 8% to ~ 5% and SSIM from 0.62 to 0.80. Human ratings verified the effectiveness of AI-based denoising, and correlation analyses showed that metric-optimized configurations generally aligned better with subjective scores than perceptual-guided ones, suggesting that conventional metrics remain a reliable reference for loss selection. In contrast, SE-Wideband-GRAPPA restoration proved challenging, yielding limited gains across networks. These findings highlight both the promise and limitations of AI-based SE-Wideband image restoration: substantial improvements are achievable for SE-Wideband, but clinical translation will require new strategies tailored to compounded artifacts in SE-GRAPPA. The curated dataset and benchmarking framework presented here provide a valuable reference for future methodological development and the broader integration of Wideband MRI into neuroimaging practice.