Simultaneous auto-calibration and gradient delays estimation (SAGE) in non-Cartesian parallel MRI using low-rank constraints
Simultaneous auto-calibration and gradient delays estimation (SAGE) in non-Cartesian parallel MRI using low-rank constraints
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DOI：
10.1002/mrm.27168
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发表时间：
2018-11-01
影响因子：
3.3
通讯作者：
Lustig, Michael
中科院分区：
文献类型：
--
作者：
Jiang, Wenwen;Larson, Peder E. Z.;Lustig, Michael
目的：为了纠正非笛卡尔MRI中的梯度定时延迟，同时恢复用于并行成像的无损坏的自动校准数据，而无需额外的校准scans.Methods：从多通道k空间数据构建的校准矩阵应该是固有的低秩。此属性用于构建重建核或灵敏度图。不同轴上的梯度硬件和RF接收链之间的延迟（在笛卡尔MRI（不包括EPI）中相对良性）导致轨迹偏差，从而导致非笛卡尔轨迹的数据不一致。这些又导致更高的秩和损坏的校准信息，这阻碍了重建。在这里，提出了一种名为同步自动校准和梯度延迟估计（SAGE）的方法，该方法估计实际的k空间轨迹，同时恢复未损坏的自动校准数据。这通过估计导致校准矩阵的最低秩的梯度延迟来完成。采用高斯-牛顿法求解非线性问题。该方法在使用中心向外径向，投影重建和螺旋轨迹的模拟进行了验证。可行性被证明与中心的径向和投影reconstruction trajectories.Results：SAGE的幻影和在体内扫描是能够估计梯度时间延迟的信噪比水平低至5的高精度。该方法是能够有效地消除伪影造成的梯度时间延迟和恢复图像质量的中心向外的径向，投影重建，和spiral trajectors.Conclusion：低秩为基础的方法同时介绍了估计梯度时间延迟，并提供准确的自动校准数据，提高图像质量，没有任何额外的校准扫描。
Purpose: To correct gradient timing delays in non-Cartesian MRI while simultaneously recovering corruption-free auto-calibration data for parallel imaging, without additional calibration scans.Methods: The calibration matrix constructed from multi-channel k-space data should be inherently low-rank. This property is used to construct reconstruction kernels or sensitivity maps. Delays between the gradient hardware across different axes and RF receive chain, which are relatively benign in Cartesian MRI (excluding EPI), lead to trajectory deviations and hence data inconsistencies for non-Cartesian trajectories. These in turn lead to higher rank and corrupted calibration information which hampers the reconstruction. Here, a method named Simultaneous Auto-calibration and Gradient delays Estimation (SAGE) is proposed that estimates the actual k-space trajectory while simultaneously recovering the uncorrupted auto-calibration data. This is done by estimating the gradient delays that result in the lowest rank of the calibration matrix. The Gauss-Newton method is used to solve the non-linear problem. The method is validated in simulations using center-out radial, projection reconstruction and spiral trajectories. Feasibility is demonstrated on phantom and in vivo scans with center-out radial and projection reconstruction trajectories.Results: SAGE is able to estimate gradient timing delays with high accuracy at a signal to noise ratio level as low as 5. The method is able to effectively remove artifacts resulting from gradient timing delays and restore image quality in center-out radial, projection reconstruction, and spiral trajectories.Conclusion: The low-rank based method introduced simultaneously estimates gradient timing delays and provides accurate auto-calibration data for improved image quality, without any additional calibration scans.