论文标题
MRI中采样方案的离线数据驱动的优化
Off-the-grid data-driven optimization of sampling schemes in MRI
论文作者
论文摘要
我们提出了一种基于学习的新算法，以在MRI中生成有效且具有物理上合理的抽样模式。与最近的基于学习的方法相比，该方法具有一些优势：i）它在网上工作和ii）允许处理任意的物理约束。这两个功能允许在抽样模式中更具多功能性，可以利用MRI扫描仪提供的所有自由度。该方法包括对由算法隐式定义的成本函数的高维优化。我们提出了各种数值工具来应对这一数字挑战。
We propose a novel learning based algorithm to generate efficient and physically plausible sampling patterns in MRI. This method has a few advantages compared to recent learning based approaches: i) it works off-the-grid and ii) allows to handle arbitrary physical constraints. These two features allow for much more versatility in the sampling patterns that can take advantage of all the degrees of freedom offered by an MRI scanner. The method consists in a high dimensional optimization of a cost function defined implicitly by an algorithm. We propose various numerical tools to address this numerical challenge.
