TY - JOUR

T1 - Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images

AU - Jensen, Henrik G.

AU - Lauze, François

AU - Darkner, Sune

N1 - Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

PY - 2022

Y1 - 2022

N2 - We present an information-theoretic approach to the registration of images with directional information, especially for diffusion-weighted images (DWIs), with explicit optimization over the directional scale. We call it locally orderless registration with directions (LORDs). 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 nonrigid deformations. 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 experimental results clearly illustrate a novel promising regularizing effect, which 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.

AB - We present an information-theoretic approach to the registration of images with directional information, especially for diffusion-weighted images (DWIs), with explicit optimization over the directional scale. We call it locally orderless registration with directions (LORDs). 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 nonrigid deformations. 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 experimental results clearly illustrate a novel promising regularizing effect, which 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.

KW - Diffusion weighted imaging

KW - Locally orderless imaging

KW - Normalized mutual information

KW - Orientation information

KW - Registration

UR - http://www.scopus.com/inward/record.url?scp=85112549208&partnerID=8YFLogxK

U2 - 10.1007/s10851-021-01050-2

DO - 10.1007/s10851-021-01050-2

M3 - Journal article

AN - SCOPUS:85112549208

VL - 64

SP - 1

EP - 16

JO - Journal of Mathematical Imaging and Vision

JF - Journal of Mathematical Imaging and Vision

SN - 0924-9907

ER -