MR-MOTUS: model-based non-rigid motion estimation for MR-guided radiotherapy using a reference image and minimal k-space data
MR-MOTUS: model-based non-rigid motion estimation for MR-guided radiotherapy using a reference image and minimal k-space data
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DOI：
10.1088/1361-6560/ab554a
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发表时间：
2020-01-01
影响因子：
3.5
通讯作者：
Sbrizzi, Alessandro
中科院分区：
文献类型：
--
作者：
Huttinga, Niek R. F.;van den Berg, Cornelis A. T.;Sbrizzi, Alessandro
由于磁共振直线加速器的出现，从MRI数据中进行时间分辨运动估计已经受到越来越多的关注。MRI扫描仪和线性加速器的组合使得能够基于从MRI数据估计的内部器官运动来调整辐射计划。然而，从MRI数据中对这种运动进行时间分辨估计仍然是一个挑战。鉴于这一应用，我们提出了MR-MOTUS，一个框架来估计非刚性的三维运动从最小的k空间数据。MR-MOTUS由两个主要部分组成：（1）将变形对象的k空间信号与非刚性运动场和参考图像明确关联的信号模型，以及（2）直接从k空间数据对非刚性运动场进行基于模型的重建。使用先验可用的参考图像和事实，即内部身体运动表现出高水平的空间相关性，我们表示在低维空间中的运动场，并从最小的k-空间数据，可以非常迅速地获取重建它们。该信号模型通过数字3D模型的数值实验进行验证，并使用各种欠采样策略从回顾性欠采样的体内头部和腹部数据重建运动场。与国家的最先进的图像配准从相同的欠采样数据重建的图像进行比较。结果表明，MR-MOTUS从474倍回顾性下采样k空间数据重建体内3D刚性头部运动，并从63倍回顾性下采样k空间数据重建体内非刚性3D呼吸运动。在自由呼吸期间用2D黄金角采集的前瞻性欠采样数据的初步结果证明了该方法的实际可行性。
Time-resolved motion estimation from MRI data has received an increasing amount of interest due to the advent of the MR-Linac. The combination of an MRI scanner and a linear accelerator enables radiation plan adaptation based on internal organ motion estimated from MRI data. However, time-resolved estimation of this motion from MRI data still remains a challenge. In light of this application, we propose MR-MOTUS, a framework to estimate non-rigid 3D motion from minimal k-space data. MR-MOTUS consists of two main components: (1) a signal model that explicitly relates the k-space signal of a deforming object to non-rigid motion-fields and a reference image, and (2) model-based reconstructions of the non-rigid motion-fields directly from k-space data. Using an a priori available reference image and the fact that internal body motion exhibits a high level of spatial correlation, we represent the motion-fields in a low-dimensional space and reconstruct them from minimal k-space data that can be acquired very rapidly. The signal model is validated through numerical experiments with a digital 3D phantom and motion-fields are reconstructed from retrospectively undersampled in vivo head and abdomen data using various undersampling strategies. A comparison is made with state-of-the-art image registration performed on images reconstructed from the same undersampled data. Results show that MR-MOTUS reconstructs in vivo 3D rigid head motion from 474-fold retrospectively downsampled k-space data, and in vivo non-rigid 3D respiratory motion from 63-fold retrospectively undersampled k-space data. Preliminary results on prospectively undersampled data acquired with a 2D golden angle acquisition during free-breathing demonstrate the practical feasibility of the method.