Deformable multi-contrast image registration is a challenging yet crucial task due to the complex, non-linear intensity relationships across different imaging contrasts. Conventional registration methods typically rely on iterative optimization of the deformation field, which is time-consuming. Although recent learning-based approaches enable fast and accurate registration during inference, their generalizability remains limited to the specific contrasts observed during training. In this work, we propose an adaptive conditional contrast-agnostic deformable image registration framework (AC-CAR) based on a random convolution-based contrast augmentation scheme. AC-CAR can generalize to arbitrary imaging contrasts without observing them during training. To encourage contrast-invariant feature learning, we propose an adaptive conditional feature modulator (ACFM) that adaptively modulates the features and the contrast-invariant latent regularization to enforce the consistency of the learned feature across different imaging contrasts. Additionally, we enable our framework to provide contrast-agnostic registration uncertainty by integrating a variance network that leverages the contrast-agnostic registration encoder to improve the trustworthiness and reliability of AC-CAR. Experimental results demonstrate that AC-CAR outperforms baseline methods in registration accuracy and exhibits superior generalization to unseen imaging contrasts. Code is available at https://github.com/Yinsong0510/AC-CAR.

翻译：多对比度形变图像配准是一项具有挑战性但至关重要的任务，这是由于不同成像对比度之间存在复杂、非线性的强度关系。传统的配准方法通常依赖于形变场的迭代优化，这一过程较为耗时。尽管近年来基于学习的方法能够在推理阶段实现快速且准确的配准，但其泛化能力仍局限于训练阶段所观察到的特定对比度。在本工作中，我们提出了一种基于随机卷积对比度增强策略的自适应条件对比无关形变图像配准框架（AC-CAR）。AC-CAR能够泛化至任意成像对比度，而无需在训练阶段观测到这些对比度。为了促进对比度不变特征的学习，我们提出了一种自适应条件特征调制器（ACFM），它能够自适应地调制特征，并通过对比度不变潜在正则化来强制学习到的特征在不同成像对比度之间保持一致性。此外，我们通过集成一个方差网络，使我们的框架能够提供对比度无关的配准不确定性，该网络利用对比度无关的配准编码器来提升AC-CAR的可信度与可靠性。实验结果表明，AC-CAR在配准精度上优于基线方法，并对未见过的成像对比度展现出卓越的泛化能力。代码发布于 https://github.com/Yinsong0510/AC-CAR。