论文标题
快速低等级柱的加速动态MRI的压缩感测
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRI
论文作者
论文摘要
这项工作通过在序列的矢量化图像形成的矩阵上假设近似LR模型来开发一种快速，记忆效率和一般算法，以加速/不足的动态MRI。总的来说，我们的意思是我们的算法可用于多个加速动态MRI应用和多个采样率（加速度）和具有单一参数选择（无参数调整）的模式。我们表明，我们提出的算法，交替的梯度下降（GD）和MRI最小化（Altgdmin-MRI和Altgdmin-MRI2），表现优于许多现有方法，同时平均也比所有方法都快。该主张基于对8种不同回顾性的无效的单层或多型动力学MRI应用的比较，该应用使用1D笛卡尔或2D伪式 - 放射线以多个采样速率进行采样。所有比较都使用相同的算法参数集。我们的第二个贡献是一个迷你批次和完全在线扩展，可以在新图像框架的测量到达或短暂延迟后，可以在新的测量结果和返回重建。
This work develops a fast, memory-efficient, and general algorithm for accelerated/undersampled dynamic MRI by assuming an approximate LR model on the matrix formed by the vectorized images of the sequence. By general, we mean that our algorithm can be used for multiple accelerated dynamic MRI applications and multiple sampling rates (acceleration rates) and patterns with a single choice of parameters (no parameter tuning). We show that our proposed algorithms, alternating Gradient Descent (GD) and minimization for MRI (altGDmin-MRI and altGDmin-MRI2), outperform many existing approaches while also being faster than all of them, on average. This claim is based on comparisons on 8 different retrospectively undersampled single- or multi-coil dynamic MRI applications, undersampled using either 1D Cartesian or 2D pseudo-radial undersampling at multiple sampling rates. All comparisons used the same set of algorithm parameters. Our second contribution is a mini-batch and a fully online extension that can process new measurements and return reconstructions either as soon as measurements of a new image frame arrive, or after a short delay.
