用新型正则化方法提升3D非笛卡尔MRI重建速度与稳定性
Weakly Convex Ridge Regularization for 3D Non-Cartesian MRI Reconstruction
- 设计旋转不变的弱凸脊正则化，结合变分法与深度学习优势
- 在多个数据集上超越传统基线，接近先进去噪器性能
- 计算效率高，对扫描参数变化更鲁棒，适合临床部署
高度加速的非笛卡尔采集协议虽显著缩短扫描时间，但常伴随漫长的重建延迟。基于深度学习的重建方法虽可缓解此问题，却往往缺乏稳定性和对分布外数据的鲁棒性。为此，本文训练了一种旋转不变的弱凸脊正则化（WCRR）。该变分重建方法在回顾性模拟数据及前瞻性GoLF SPARKLING和CAIPIRINHA采集数据上进行了评估。结果表明，该方法始终优于广泛使用的基线模型，并达到与采用先进3D DRUNet去噪器的“插件式”重建相当的性能，同时具备显著更高的计算效率和对采集参数变化的鲁棒性。综上，WCRR融合了原理严谨的变分方法与现代深度学习方法的优势。
原文摘要 · Abstract (English)
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.
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