Department of Applied Mathematics, University of Leeds, Leeds, UK.
Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK.
MAGMA. 2021 Dec;34(6):805-822. doi: 10.1007/s10334-021-00936-x. Epub 2021 Jun 23.
Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR).
MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data).
DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HD = 9.98 ± 9.76, HD = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses.
MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.
基于模型的配准(MDR)是一种通用方法,可以去除定量成像中的患者运动。在这项研究中,我们研究了 MDR 是否可以有效地纠正自由呼吸磁共振肾图(MRR)中的运动。
将 MDR 推广到线性示踪动力学模型,并使用二维或三维自由形态变形(FFD)进行实现,采用多分辨率和梯度下降优化。使用肾脏模拟数字参考对象(DRO)和高时间分辨率的多切片 2D(5 名患者)和 3D 采集(8 名患者)的自由呼吸患者数据评估 MDR。通过与地面真相 DRO 进行比较,计算地面真相掩模与分割之间的 Hausdorff 距离(HD),以及动态图像、信号时间过程和参数图(所有数据)的视觉评估来评估配准准确性。
DRO 数据表明,MDR 后参数图的偏差和精度与无运动数据无法区分。MDR 导致 HD 降低(HD=9.98±9.76,HD=1.63±0.49)。视觉检查表明,MDR 有效地消除了动态数据中的运动效应,导致参数图上解剖结构的清晰度提高,并减少了信号时间过程中运动引起的波动。
MDR 为合成和患者数据中的 MRR 提供了有效的运动校正。需要进一步的工作来比较其与其他更成熟方法的性能。