Image registration is fundamental in medical imaging applications, such as disease progression analysis or radiation therapy planning. The primary objective of image registration is to precisely capture the deformation between two or more images, typically achieved by minimizing an optimization problem. Due to its inherent ill-posedness, regularization is a key component in driving the solution toward anatomically meaningful deformations. A wide range of regularization methods has been proposed for both conventional and deep learning-based registration. However, the appropriate application of regularization techniques often depends on the specific registration problem, and no one-fits-all method exists. Despite its importance, regularization is often overlooked or addressed with default approaches, assuming existing methods are sufficient. A comprehensive and structured review remains missing. This review addresses this gap by introducing a novel taxonomy that systematically categorizes the diverse range of proposed regularization methods. It highlights the emerging field of learned regularization, which leverages data-driven techniques to automatically derive deformation properties from the data. Moreover, this review examines the transfer of regularization methods from conventional to learning-based registration, identifies open challenges, and outlines future research directions. By emphasizing the critical role of regularization in image registration, we hope to inspire the research community to reconsider regularization strategies in modern registration algorithms and to explore this rapidly evolving field further.

翻译：图像配准是医学成像应用中的基础技术，如疾病进展分析或放射治疗规划。图像配准的主要目标是通过最小化优化问题，精确捕捉两幅或多幅图像之间的形变。由于其固有的不适定性，正则化是驱动解向解剖学意义形变发展的关键组成部分。针对传统和基于深度学习的配准方法，已提出了广泛的正则化方法。然而，正则化技术的适当应用通常取决于具体的配准问题，并不存在适用于所有情况的方法。尽管正则化至关重要，但它常被忽视或采用默认方法处理，假设现有方法已足够充分。目前仍缺乏全面且结构化的综述。本综述通过引入一种新颖的分类法，系统地对已提出的各类正则化方法进行分类，从而填补了这一空白。它重点介绍了新兴的学习型正则化领域，该领域利用数据驱动技术从数据中自动推导形变特性。此外，本综述探讨了正则化方法从传统配准到基于学习的配准的迁移，识别了未解决的挑战，并概述了未来的研究方向。通过强调正则化在图像配准中的关键作用，我们希望激发研究界重新思考现代配准算法中的正则化策略，并进一步探索这一快速发展的领域。