发表机构
Chemnitz University of Technology（开姆尼茨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CLEAR是首个用于4D血流CMR重建的学习型显式解析正则化器，结合压缩感知可解释性与学习模型灵活性，在超加速10-50倍下优于LLR和FlowVN，参数少于1万。
AI 中文摘要
虽然压缩感知正则化器通过透明的变分目标实现了4D血流心脏磁共振的可解释重建，但其手工设计的特性在高加速下限制性过强。最先进的基于学习的方法缓解了这一问题，但通常通过展开网络模块隐式编码正则化，这限制了其可解释性。为解决这一局限，我们提出CLEAR，旨在结合压缩感知的可解释性与学习模型的灵活性。据我们所知，这是首个用于4D重建任务的学习正则化器。在CMRx4DFlow2026挑战赛的超加速10倍至50倍场景中，CLEAR优于压缩感知局部低秩（LLR）和流行的变分网络FlowVN，同时使用少于1万个参数并保持可解释的正则化结构。
英文摘要
While compressed-sensing regularizers enable interpretable reconstruction of 4D Flow CMR through transparent variational objectives, their hand-crafted nature is too restrictive under high acceleration. State-of-the-art learning-based approaches mitigate this, but typically encode regularization implicitly through unrolled network modules, which limits their interpretability. To address this limitation, we propose CLEAR, designed to combine the interpretability of compressed sensing with the flexibility of learned models. To the best of our knowledge, it is the first learned regularizer for a 4D reconstruction task. In the ultra-accelerated \(10\times\)--\(50\times\) regime of the CMRx4DFlow2026 challenge, CLEAR outperforms compressed sensing locally low-rank (LLR) and the popular variational network FlowVN, while using less than 10k parameters and preserving an interpretable regularization structure.
Comments8 figures, 1 table
