The deformable registration of images of different modalities, essential in many medical imaging applications, remains challenging. The main challenge is developing a robust measure for image overlap despite the compared images capturing different aspects of the underlying tissue. Here, we explore similarity metrics based on functional dependence between intensity values of registered images. Although functional dependence is too restrictive on the global scale, earlier work has shown competitive performance in deformable registration when such measures are applied over small enough contexts. We confirm this finding and further develop the idea by modeling local functional dependence via the linear basis function model with the basis functions learned jointly with the deformation. The measure can be implemented via convolutions, making it efficient to compute on GPUs. We release the method as an easy-to-use tool and show good performance on three datasets compared to well-established baseline and earlier functional dependence-based methods.

翻译：不同模态图像的形变配准在众多医学影像应用中至关重要，但依然具有挑战性。主要挑战在于，尽管被比较的图像捕捉的是底层组织的不同方面，仍需开发一种稳健的图像重叠度量方法。本文探索了基于配准图像强度值之间函数依赖关系的相似性度量。尽管函数依赖关系在全局尺度上限制过强，但先前研究表明，当此类度量应用于足够小的局部上下文时，在形变配准中能表现出有竞争力的性能。我们验证了这一发现，并通过线性基函数模型进一步拓展该思路，该模型利用与形变联合学习的基函数来建模局部函数依赖关系。该度量可通过卷积运算实现，从而能在GPU上高效计算。我们将该方法发布为一款易于使用的工具，并在三个数据集上展示了其相对于成熟基线方法及早期基于函数依赖的方法的优异性能。