无需调参即可快速重建多种动态MRI数据，兼顾精度与低延迟。
Low Latency and Generalizable Dynamic MRI via L+S Alternating GD and Minimization
- 采用低秩与稀疏分离交替优化算法，简化模型参数。
- 在多种采样方案和速率下保持高重建精度与低延迟。
- 适合临床动态MRI实时应用，尤其对参数调优敏感场景
本文提出新型MRI重建方法，适用于大量动态MRI应用、采样方案及采样率，具备高精度、快速且低延迟特性，且无需针对具体问题调整参数。我们定义这种单一算法在无参数调优情况下对多种设置均表现良好为泛化能力。该能力仅可能存在于结构简单的模型（如低秩LR或低秩加稀疏L+S）及其对应的简单少参数算法中。本文开发并评估了此类基于LR或L+S的简洁算法。
原文摘要 · Abstract (English)
In this work, we develop novel MRI reconstruction approaches that are accurate, fast and low-latency for a large number of dynamic MRI applications, sampling schemes and sampling rates; without any problem-specific parameter tuning. We refer to this property of a single algorithm, without parameter tuning, being accurate and fast for many settings as generalizability. Generalizability is possible only for simple (few parameter) models such as low-rank (LR) or LR plus sparse (L plus S), and for simple few parameter algorithms based on these models, which is what we develop and evaluate in this work.
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