Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today's limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.

中文翻译：

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动态肾脏核磁共振图像配准的现状和前景。

磁共振成像（MRI）方式已在慢性肾脏疾病（CKD）的临床检查中发挥了越来越重要的作用。这包括通过动脉自旋标记（ASL）或动态对比增强（DCE-）MRI对血液动力学参数的评估。尤其是在后者中，肾脏的图像或体积会在长达几分钟的时间内随时间获取。因此，它们受到运动的阻碍，例如受到脉动，蠕动或呼吸运动的阻碍。此运动可能会妨碍后续的图像分析来估计血液动力学参数，例如肾血流或肾小球滤过率（GFR）。为了克服时间分辨肾脏MRI中的运动伪影，已经提出了多种策略。肾图像配准方法可分为（1）图像采集技术，（2）后处理方法，或（3）图像采集和后处理方法的组合。尽管取得了数十年的进步，但临床实践中的翻译仍然缺失。本文的目的是讨论有关肾脏图像配准技术的现有文献，并显示当今所提议的技术阻碍临床翻译的局限性。本文将转换，标准函数和搜索类型作为传统组件以及基于深度学习的新兴注册技术包括在内。当前的趋势表明更快的注册和更准确的结果。但是，仍缺乏对肾脏MRI中图像配准的标准化评估。本文的目的是讨论有关肾脏图像配准技术的现有文献，并显示当今所提议的技术阻碍临床翻译的局限性。本文将转换，标准函数和搜索类型作为传统组件以及基于深度学习的新兴注册技术包括在内。当前的趋势表明更快的注册和更准确的结果。但是，仍缺乏对肾脏MRI中图像配准的标准化评估。本文的目的是讨论有关肾脏图像配准技术的现有文献，并显示当今所提议的技术阻碍临床翻译的局限性。本文将转换，标准函数和搜索类型作为传统组件以及基于深度学习的新兴注册技术包括在内。当前的趋势表明更快的注册和更准确的结果。但是，仍缺乏对肾脏MRI中图像配准的标准化评估。搜索类型作为传统组成部分以及基于深度学习的新兴注册技术。当前的趋势表明更快的注册和更准确的结果。但是，仍缺乏对肾脏MRI中图像配准的标准化评估。搜索类型作为传统组成部分以及基于深度学习的新兴注册技术。当前的趋势表明更快的注册和更准确的结果。但是，仍缺乏对肾脏MRI中图像配准的标准化评估。