Introduction

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).

Materials and methods

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).

Results

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_unregistered = 9.98 ± 9.76, HD_registered = 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.

Discussion

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.

中文翻译：

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使用模型驱动配准对自由呼吸磁共振肾图进行运动校正

 介绍

模型驱动配准（MDR）是定量成像中消除患者运动的通用方法。在本研究中，我们研究 MDR 是否可以有效纠正自由呼吸磁共振肾图 (MRR) 中的运动。

 材料和方法

MDR 被推广到线性示踪动力学模型，并使用具有多分辨率和梯度下降优化的 2D 或 3D 自由变形 (FFD) 来实现。使用模拟肾脏的数字参考对象 (DRO) 和在多切片 2D（5 名患者）和 3D 采集（8 名患者）中以高时间分辨率采集的自由呼吸患者数据来评估 MDR。通过与地面实况 DRO 进行比较、计算地面实况掩模之间的豪斯多夫距离 (HD)、动态图像、信号时间过程和参数图（所有数据）的分割和视觉评估来评估配准精度。

 结果

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 减少（_未注册的HD = 9.98 ± 9.76，_已注册的HD = 1.63 ± 0.49）。目视检查表明，MDR 有效消除了动态数据中的运动影响，从而显着改善了参数图上的解剖轮廓，并减少了信号时间过程中运动引起的振荡。

 讨论

MDR 可对合成数据和患者数据中的 MRR 进行有效的运动校正。未来的工作需要将其性能与其他更成熟的方法进行比较。