LDDMM

Anatomical Data Augmentation via Fluid-based Image Registration

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our …

Fluid registration between lung CT and stationary chest tomosynthesis images

Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. However, while high-quality computed tomography (CT) images are often …

Region-specific Diffeomorphic Metric Mapping

We introduce a region-specific diffeomorphic metric mapping (RDMM) registration approach. RDMM is non-parametric, estimating spatio-temporal velocity fields which parameterize the sought-for spatial transformation. Regularization of these velocity …

Memory Efficient LDDMM for Lung CT

In this paper a novel Large Deformation Diffeomorphic Metric Mapping (LDDMM) scheme is presented which has significantly lower computational and memory demands than standard LDDMM but achieves the same accuracy. We exploit the smoothness of …

Uncertainty Quantification for LDDMM Using a Low-Rank Hessian Approximation

This paper presents an approach to estimate the uncertainty of registration parameters for the large displacement diffeomorphic metric mapping (LDDMM) registration framework. Assuming a local multivariate Gaussian distribution as an approximation for …

Large deformation diffeomorphic registration of diffusion-weighted imaging data

We seek to compute a diffeomorphic map between a pair of diffusion-weighted images under large deformation. Unlike existing techniques, our method allows any diffusion model to be fitted after registration for subsequent multifaceted analysis. This …

Large Deformation Diffeomorphic Registration of Diffusion-Weighted Images with Explicit Orientation Optimization

We seek to compute a diffeomorphic map between a pair of diffusion-weighted images under large deformation. Unlike existing techniques, our method allows any diffusion model to be fitted after registration for subsequent multifaceted analysis. This …

Riemannian metrics for statistics on shapes: parallel transport and scale invariance

To be able to statistically compare evolutions of image timeseries data requires a method to express these evolutions in a common coordinate system. This requires a mechanism to transport evolutions between coordinate systems: e.g., parallel …

Large Deformation Diffeomorphic Registration of Diffusion-Weighted Images

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 …

Geodesic Regression for Image Time-Series

Registration of image-time series has so far been accomplished (i) by concatenating registrations between image pairs, (ii) by solving a joint estimation problem resulting in piecewise geodesic paths between image pairs, (iii) by kernel based local …