While highly accelerated non-Cartesian acquisition protocols significantly reduce scan time, they often entail long reconstruction delays. Deep learning based reconstruction methods can alleviate this, but often lack stability and robustness to distribution shifts. As an alternative, we train a rotation invariant weakly convex ridge regularizer (WCRR). The resulting variational reconstruction approach is benchmarked against state of the art methods on retrospectively simulated data and (out of distribution) on prospective GoLF SPARKLING and CAIPIRINHA acquisitions. Our approach consistently outperforms widely used baselines and achieves performance comparable to Plug and Play reconstruction with a state of the art 3D DRUNet denoiser, while offering substantially improved computational efficiency and robustness to acquisition changes. In summary, WCRR unifies the strengths of principled variational methods and modern deep learning based approaches.

翻译：虽然高度加速的非笛卡尔采集协议能显著缩短扫描时间，但往往导致冗长的重建延迟。基于深度学习的重建方法可缓解此问题，但常缺乏稳定性和应对分布偏移的鲁棒性。为此，我们训练了一种旋转不变的弱凸脊正则化器（WCRR）。基于该正则化器的变分重建方法在回顾性模拟数据及（分布外）前瞻性GoLF SPARKLING与CAIPIRINHA采集数据上，与当前最优方法进行了基准对比。所提方法始终优于广泛使用的基线方法，且在性能上可与采用先进3D DRUNet去噪器的即插即用重建相媲美，同时大幅提升了计算效率及对采集参数变化的鲁棒性。总之，WCRR融合了原则性变分方法与现代深度学习方法的优势。