Large deformation diffeomorphic registration of diffusion-weighted images

Authors:
Pei Zhang;Marc Niethammer;Dinggang Shen;Pew-Thian Yap
Affiliations:
Department of Radiology, The University of North Carolina at Chapel Hill;Department of Computer Science, Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill;Department of Radiology, The University of North Carolina at Chapel Hill;Department of Radiology, The University of North Carolina at Chapel Hill
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

Citing 4
Cited 1

Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms

International Journal of Computer Vision
Diffusion MRI Registration Using Orientation Distribution Functions

IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Feasibility and advantages of diffusion weighted imaging atlas construction in Q-space

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Diffeomorphic 3D Image Registration via Geodesic Shooting Using an Efficient Adjoint Calculation

International Journal of Computer Vision

Diffeomorphic metric mapping of hybrid diffusion imaging based on BFOR signal basis

IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

Registration of Diffusion-weighted imaging (DWI) data emerges as an important topic in magnetic resonance (MR) image analysis. As existing methods are often designed for specific diffusion models, it is difficult to fit to the registered data different models other than the one used for registration. In this paper we describe a diffeomorphic registration algorithm for DWI data in a large deformation setting. Our method generates spatially normalized DWI data and it is thus possible to fit various diffusion models after registration for comparison purposes. Our algorithm includes (1) a reorientation component, where each diffusion profile (DWI signal as a function on a unit sphere) is decomposed, reoriented and recomposed to form the orientation-corrected DWI profile, and (2) a large deformation diffeomorphic registration component to ensure one-to-one mapping in a large-structural-variation scenario. In addition our algorithm uses a geodesic shooting mechanism to avoid the huge computational resources that are needed to register high-dimensional vector-valued data. We also incorporate into our algorithm a multi-kernel strategy where anatomical structures at different scales are considered simultaneously during registration. We demonstrate the efficacy of our method using in vivo data.