14,412 research outputs found
Distributed-memory large deformation diffeomorphic 3D image registration
We present a parallel distributed-memory algorithm for large deformation
diffeomorphic registration of volumetric images that produces large isochoric
deformations (locally volume preserving). Image registration is a key
technology in medical image analysis. Our algorithm uses a partial differential
equation constrained optimal control formulation. Finding the optimal
deformation map requires the solution of a highly nonlinear problem that
involves pseudo-differential operators, biharmonic operators, and pure
advection operators both forward and back- ward in time. A key issue is the
time to solution, which poses the demand for efficient optimization methods as
well as an effective utilization of high performance computing resources. To
address this problem we use a preconditioned, inexact, Gauss-Newton- Krylov
solver. Our algorithm integrates several components: a spectral discretization
in space, a semi-Lagrangian formulation in time, analytic adjoints, different
regularization functionals (including volume-preserving ones), a spectral
preconditioner, a highly optimized distributed Fast Fourier Transform, and a
cubic interpolation scheme for the semi-Lagrangian time-stepping. We
demonstrate the scalability of our algorithm on images with resolution of up to
on the "Maverick" and "Stampede" systems at the Texas Advanced
Computing Center (TACC). The critical problem in the medical imaging
application domain is strong scaling, that is, solving registration problems of
a moderate size of ---a typical resolution for medical images. We are
able to solve the registration problem for images of this size in less than
five seconds on 64 x86 nodes of TACC's "Maverick" system.Comment: accepted for publication at SC16 in Salt Lake City, Utah, USA;
November 201
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.Comment: Add new discussion
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