We present an information-theoretic appro\-ach to the registration of images with directional information, and especially for diffusion-Weighted Images (DWI), with explicit optimization over the directional scale. We call it Locally Orderless Registration with Directions (LORD). We focus on normalized mutual information as a robust information-theoretic similarity measure for DWI. The framework is an extension of the LOR-DWI density-based hierarchical scale-space model that varies and optimizes the integration, spatial, directional, and intensity scales. As affine transformations are insufficient for inter-subject registration, we extend the model to non-rigid deformations. We show that the formulation provides intrinsic regularization through the orientational information. We illustrate that the proposed model deforms orientation distribution functions (ODFs) correctly and is capable of handling the classic complex challenges in DWI-registrations, such as the registration of fiber-crossings along with kissing, fanning, and interleaving fibers. Our results clearly illustrate a novel promising regularizing effect, that comes from the nonlinear orientation-based cost function. We show the properties of the different image scales and, we show that including orientational information in our model makes the model better at retrieving deformations in contrast to standard scalar-based registration.
翻译:我们为图像登记提供了一种带有方向性信息的信息理论近似度,特别是用于扩散-加权图像(DWI)的图像登记,并明确优化了方向规模。我们称其为“无秩序无秩序地向方向登记”(LORD)。我们注重将相互信息正常化,以此作为DWI的稳健信息理论相似性衡量标准。这个框架是LOR-DWI基于密度的等级空间模型的延伸,该模型与接吻、扇风、和内分流纤维相容,并优化其集成、空间、方向和强度尺度。由于对对象间登记而言,亲近转换不够,我们将该模型扩展为非硬性畸形。我们表明,该模型通过定向信息提供了内在的正规化。我们表明,拟议模型向方向分配功能是正确的,能够应对DWI登记过程中的典型复杂挑战,例如,在接吻、扇风和内分层纤维的注册。我们的结果清楚地表明了一种有创意的正规化效果,这种效果来自非线方向向方向的模型,包括基于成本方向的模型。我们展示了不同的标准格式的变形图。我们展示了不同格式的特性,在模型上显示了不同格式的变形图的特性。