PDF(Pubmed)
Abstract：
OBJECTIVE: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-encoded FID navigator) in each radial spoke.
METHODS: A forward model was created from low-resolution calibration data to simulate the effect of relative motion between the coil sensitivity profiles and the underlying object on the self-encoded FID navigator signal. Trajectory deviations were included in the model as low spatial-order field variations. Three volunteers were imaged at 3 T using a modified 3D gradient-echo sequence acquired with a Kooshball trajectory while performing abrupt and continuous head motion. Rigid body-motion parameters were estimated from the central k-space signal of each spoke using a least-squares fitting algorithm. The accuracy of self-navigated motion parameters was assessed relative to an established external tracking system. Quantitative image quality metrics were computed for images with and without retrospective correction using external and self-navigated motion measurements.
RESULTS: Self-encoded FID navigators achieved mean absolute errors of 0.69 ± 0.82 mm and 0.73 ± 0.87° relative to external tracking for maximum motion amplitudes of 12 mm and 10°. Retrospective correction of the 3D radial data resulted in substantially improved image quality for both abrupt and continuous motion paradigms, comparable to external tracking results.
CONCLUSIONS: Accurate rigid body motion parameters can be rapidly obtained from self-encoded FID navigator signals in 3D radial MRI to continuously correct for head movements. This approach is suitable for robust neuroanatomical imaging in subjects that exhibit patterns of large and frequent motion.
METHODS: A forward model was created from low-resolution calibration data to simulate the effect of relative motion between the coil sensitivity profiles and the underlying object on the self-encoded FID navigator signal. Trajectory deviations were included in the model as low spatial-order field variations. Three volunteers were imaged at 3 T using a modified 3D gradient-echo sequence acquired with a Kooshball trajectory while performing abrupt and continuous head motion. Rigid body-motion parameters were estimated from the central k-space signal of each spoke using a least-squares fitting algorithm. The accuracy of self-navigated motion parameters was assessed relative to an established external tracking system. Quantitative image quality metrics were computed for images with and without retrospective correction using external and self-navigated motion measurements.
RESULTS: Self-encoded FID navigators achieved mean absolute errors of 0.69 ± 0.82 mm and 0.73 ± 0.87° relative to external tracking for maximum motion amplitudes of 12 mm and 10°. Retrospective correction of the 3D radial data resulted in substantially improved image quality for both abrupt and continuous motion paradigms, comparable to external tracking results.
CONCLUSIONS: Accurate rigid body motion parameters can be rapidly obtained from self-encoded FID navigator signals in 3D radial MRI to continuously correct for head movements. This approach is suitable for robust neuroanatomical imaging in subjects that exhibit patterns of large and frequent motion.
摘要：
目的：开发一种用于3D径向MRI的自导航运动补偿策略，该策略可以通过从每个径向辐条中的中心k空间采集点（自编码FID导航器）以高时间分辨率测量刚体运动参数来补偿连续的头部运动。
方法：从低分辨率校准数据创建了正向模型，以模拟线圈灵敏度轮廓和底层物体之间的相对运动对自编码FID导航信号的影响。轨迹偏差作为低空间顺序场变化包括在模型中。使用使用Kooshball轨迹获得的修改后的3D梯度回波序列在3T下对三名志愿者进行成像，同时进行突然和连续的头部运动。使用最小二乘拟合算法从每个辐条的中心k空间信号估计刚性身体运动参数。相对于已建立的外部跟踪系统，评估了自导航运动参数的准确性。使用外部和自导航运动测量来计算具有和不具有回顾性校正的图像的定量图像质量度量。
结果：自编码的FID导航仪相对于12mm和10°的最大运动幅度相对于外部跟踪实现了0.69±0.82mm和0.73±0.87°的平均绝对误差。对3D径向数据进行回顾性校正可大大改善突然和连续运动范例的图像质量。与外部跟踪结果相当。
结论：可以从3D径向MRI中的自编码FID导航器信号中快速获得准确的刚体运动参数，以连续校正头部运动。这种方法适用于表现出大且频繁运动模式的受试者的鲁棒神经解剖成像。
方法：从低分辨率校准数据创建了正向模型，以模拟线圈灵敏度轮廓和底层物体之间的相对运动对自编码FID导航信号的影响。轨迹偏差作为低空间顺序场变化包括在模型中。使用使用Kooshball轨迹获得的修改后的3D梯度回波序列在3T下对三名志愿者进行成像，同时进行突然和连续的头部运动。使用最小二乘拟合算法从每个辐条的中心k空间信号估计刚性身体运动参数。相对于已建立的外部跟踪系统，评估了自导航运动参数的准确性。使用外部和自导航运动测量来计算具有和不具有回顾性校正的图像的定量图像质量度量。
结果：自编码的FID导航仪相对于12mm和10°的最大运动幅度相对于外部跟踪实现了0.69±0.82mm和0.73±0.87°的平均绝对误差。对3D径向数据进行回顾性校正可大大改善突然和连续运动范例的图像质量。与外部跟踪结果相当。
结论：可以从3D径向MRI中的自编码FID导航器信号中快速获得准确的刚体运动参数，以连续校正头部运动。这种方法适用于表现出大且频繁运动模式的受试者的鲁棒神经解剖成像。
