62 research outputs found
Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration
Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results
generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as
reduced computation time
LDDMM y GANs: Redes Generativas Antagónicas para Registro Difeomorfico.
El Registro Difeomorfico de imágenes es un problema clave para muchas aplicaciones de la Anatomía Computacional. Tradicionalmente, el registro deformable de imagen ha sido formulado como un problema variacional, resoluble mediante costosos métodos de optimización numérica. En la última década, contribuciones en la forma de nuevos métodos basados en formulaciones tradicionales están decreciendo, mientras que más modelos basados en Aprendizaje profundo están siendo desarrollados para aprender registros deformables de imágenes. En este trabajo contribuimos a esta nueva corriente proponiendo un novedoso método LDDMM para registro difeomorfico de imágenes 3D, basado en redes generativas antagónicas. Combinamos las arquitecturas de generadores y discriminadores con mejores prestaciones en registro deformable con el paradigma LDDMM. Hemos implementado con éxito tres modelos para distintas parametrizaciones de difeomorfismos, los cuales demuestran resultados competitivos en comparación con métodos del estado del arte tanto tradicionales como basados en aprendizaje profundo.<br /
PDE-constrained LDDMM via geodesic shooting and inexact Gauss-Newton-Krylov optimization using the incremental adjoint Jacobi equations
The class of non-rigid registration methods proposed in the framework of
PDE-constrained Large Deformation Diffeomorphic Metric Mapping is a
particularly interesting family of physically meaningful diffeomorphic
registration methods. Inexact Newton-Krylov optimization has shown an excellent
numerical accuracy and an extraordinarily fast convergence rate in this
framework. However, the Galerkin representation of the non-stationary velocity
fields does not provide proper geodesic paths. In this work, we propose a
method for PDE-constrained LDDMM parameterized in the space of initial velocity
fields under the EPDiff equation. The derivation of the gradient and the
Hessian-vector products are performed on the final velocity field and
transported backward using the adjoint and the incremental adjoint Jacobi
equations. This way, we avoid the complex dependence on the initial velocity
field in the derivations and the computation of the adjoint equation and its
incremental counterpart. The proposed method provides geodesics in the
framework of PDE-constrained LDDMM, and it shows performance competitive to
benchmark PDE-constrained LDDMM and EPDiff-LDDMM methods
CLAIRE -- Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications
We study the performance of CLAIRE -- a diffeomorphic multi-node, multi-GPU
image-registration algorithm, and software -- in large-scale biomedical imaging
applications with billions of voxels. At such resolutions, most existing
software packages for diffeomorphic image registration are prohibitively
expensive. As a result, practitioners first significantly downsample the
original images and then register them using existing tools. Our main
contribution is an extensive analysis of the impact of downsampling on
registration performance. We study this impact by comparing full-resolution
registrations obtained with CLAIRE to lower-resolution registrations for
synthetic and real-world imaging datasets. Our results suggest that
registration at full resolution can yield a superior registration quality --
but not always. For example, downsampling a synthetic image from to
decreases the Dice coefficient from 92% to 79%. However, the
differences are less pronounced for noisy or low-contrast high-resolution
images. CLAIRE allows us not only to register images of clinically relevant
size in a few seconds but also to register images at unprecedented resolution
in a reasonable time. The highest resolution considered is CLARITY images of
size . To the best of our knowledge, this is the
first study on image registration quality at such resolutions.Comment: 32 pages, 9 tables, 8 figure
A Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration
Nonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a regularisation method capable of selectively driving solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. This penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that this method is able to significantly reduce local volume changes and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst simultaneously matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Extensive leveraging of GPU parallelisation has allowed us to solve this highly computationally intensive optimisation problem while maintaining reasonable run times of under half an hour
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