CLAIRE: A distributed-memory solver for constrained large deformation diffeomorphic image registration

08/13/2018
by   Andreas Mang, et al.
0

We introduce CLAIRE, a distributed-memory algorithm and software for solving constrained large deformation diffeomorphic image registration problems in three dimensions. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss--Newton--Krylov scheme. We exploit state-of-the-art techniques in scientific computing to develop an effective solver that scales to thousand of distributed memory nodes on high-end clusters. Our improved, parallel implementation features parameter-, scale-, and grid-continuation schemes to speedup the computations and reduce the likelihood to get trapped in local minima. We also implement an improved preconditioner for the reduced space Hessian to speedup the convergence. We test registration performance on synthetic and real data. We demonstrate registration accuracy on 16 neuroimaging datasets. We compare the performance of our scheme against different flavors of the DEMONS algorithm for diffeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5x with a peak performance of up to 17x compared to our former work.

READ FULL TEXT

page 5

page 19

research
04/07/2016

A Semi-Lagrangian two-level preconditioned Newton-Krylov solver for constrained diffeomorphic image registration

We propose an efficient numerical algorithm for the solution of diffeomo...
research
03/02/2015

Constrained H^1-regularization schemes for diffeomorphic image registration

We propose regularization schemes for deformable registration and effici...
research
08/27/2014

An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration

We propose numerical algorithms for solving large deformation diffeomorp...
research
03/12/2018

Effective Implementation of GPU-based Revised Simplex algorithm applying new memory management and cycle avoidance strategies

Graphics Processing Units (GPUs) with high computational capabilities us...
research
08/28/2020

Multi-Node Multi-GPU Diffeomorphic Image Registration for Large-Scale Imaging Problems

We present a Gauss-Newton-Krylov solver for large deformation diffeomorp...
research
04/27/2018

A matrix-free approach to parallel and memory-efficient deformable image registration

We present a novel computational approach to fast and memory-efficient d...
research
07/13/2018

Newton-Krylov PDE-constrained LDDMM in the space of band-limited vector fields

PDE-constrained Large Deformation Diffeomorphic Metric Mapping is a part...

Please sign up or login with your details

Forgot password? Click here to reset