Department of Radiology, University of Iowa, Iowa City, Iowa.
Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa.
Magn Reson Med. 2018 Oct;80(4):1605-1613. doi: 10.1002/mrm.27148. Epub 2018 Feb 28.
To reconstruct artifact-free images from measured k-space data, when the actual k-space trajectory deviates from the nominal trajectory due to gradient imperfections.
Trajectory errors arising from eddy currents and gradient delays introduce phase inconsistencies in several fast scanning MR pulse sequences, resulting in image artifacts. The proposed algorithm provides a novel framework to compensate for this phase distortion. The algorithm relies on the construction of a multi-block Hankel matrix, where each block is constructed from k-space segments with the same phase distortion. In the presence of spatially smooth phase distortions between the segments, the complete block-Hankel matrix is known to be highly low-rank. Since each k-space segment is only acquiring part of the k-space data, the reconstruction of the phase compensated image from their partially parallel measurements is posed as a structured low-rank matrix optimization problem, assuming the coil sensitivities to be known.
The proposed formulation is tested on radial acquisitions in several settings including partial Fourier and golden-angle acquisitions. The experiments demonstrate the ability of the algorithm to successfully remove the artifacts arising from the trajectory errors, without the need for trajectory or phase calibration. The quality of the reconstruction was comparable to corrections achieved using the Trajectory Auto-Corrected Image Reconstruction (TrACR) for radial acquisitions.
The proposed method provides a general framework for the recovery of artifact-free images from radial trajectories without the need for trajectory calibration.
当由于梯度不完美而导致实际 k 空间轨迹偏离标称轨迹时,从测量的 k 空间数据中重建无伪影图像。
涡流和梯度延迟引起的轨迹误差会导致几种快速扫描磁共振脉冲序列中的相位不一致,从而导致图像伪影。所提出的算法提供了一种补偿这种相位失真的新框架。该算法依赖于构建多块汉克尔矩阵,其中每个块由具有相同相位失真的 k 空间段构建。在段之间存在空间平滑相位失真的情况下,已知完整的块汉克尔矩阵具有高度低秩。由于每个 k 空间段仅采集 k 空间数据的一部分,因此从它们的部分并行测量中重建相位补偿图像被表述为结构化低秩矩阵优化问题,假设已知线圈灵敏度。
在所提出的公式中,在包括部分傅里叶和黄金角采集在内的几种设置中对径向采集进行了测试。实验证明了该算法成功去除轨迹误差引起的伪影的能力,而无需进行轨迹或相位校准。重建的质量与使用径向采集的轨迹自动校正图像重建(Trajectory Auto-Corrected Image Reconstruction,TrACR)实现的校正相当。
该方法为从没有轨迹校准的径向轨迹中恢复无伪影图像提供了一个通用框架。