IEEE Trans Med Imaging. 2020 Dec;39(12):3933-3943. doi: 10.1109/TMI.2020.3008329. Epub 2020 Nov 30.
We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from undersampled measurements; extensive cardiac and respiratory motion often results in the Casorati matrix not being sufficiently low-rank. Therefore, we exploit the non-linear structure of the dynamic data, which gives the low-rank kernel matrix. Unlike prior work that rely on navigators to estimate the manifold structure, we propose a kernel low-rank matrix completion method to directly fill in the missing k-space data from variable density spiral acquisitions. We validate the proposed scheme using simulated data and in-vivo data. Our results show that the proposed scheme provides improved reconstructions compared to the classical methods such as low-rank and XD-GRASP. The comparison with breath-held cine data shows that the quantitative metrics agree, whereas the image quality is marginally lower.
我们提出了一种内核低秩算法，可从无明确 k 空间导航仪的螺旋采集恢复自由呼吸和无门控动态 MRI。低秩方法通常难以从欠采样测量中恢复自由呼吸和无门控图像；广泛的心脏和呼吸运动通常会导致 Casorati 矩阵不够低秩。因此，我们利用动态数据的非线性结构，得到低秩核矩阵。与依赖导航仪估计流形结构的先前工作不同，我们提出了一种核低秩矩阵补全方法，可直接从变密度螺旋采集填充缺失的 k 空间数据。我们使用模拟数据和体内数据验证了所提出的方案。我们的结果表明，与低秩和 XD-GRASP 等经典方法相比，所提出的方案提供了改进的重建。与屏气电影数据的比较表明，定量指标一致，而图像质量略有降低。
