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Related Experiment Video

Updated: Oct 16, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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CLAIRE: A DISTRIBUTED-MEMORY SOLVER FOR CONSTRAINED LARGE DEFORMATION DIFFEOMORPHIC IMAGE REGISTRATION.

Andreas Mang1, Amir Gholami2, Christos Davatzikos3

  • 1Department of Mathematics, University of Houston, Houston, TX 77204-5008.

SIAM Journal on Scientific Computing : a Publication of the Society for Industrial and Applied Mathematics
|October 15, 2021
PubMed
Summary
This summary is machine-generated.

We introduce CLAIRE, a scalable solver for 3D diffeomorphic image registration, achieving high accuracy and speed. This new method significantly accelerates the analysis of medical images like neuroimaging datasets.

Keywords:
35Q9349J2065F0865K1068U1076D55KKT preconditionerLDDMMNewton–Krylov methodPDE-constrained optimizationdiifeomorphic image registrationdistributed-memory algorithmoptimal control

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Area of Science:

  • Medical Image Analysis
  • Computational Science
  • Scientific Computing

Background:

  • Large deformation diffeomorphic image registration is crucial for medical image analysis.
  • Existing methods often face scalability challenges in 3D.
  • Efficient solvers are needed for clinically relevant datasets.

Purpose of the Study:

  • To release CLAIRE, a distributed-memory implementation of an effective solver for 3D constrained large deformation diffeomorphic image registration.
  • To present the formulation, algorithmic features, and software package of CLAIRE.
  • To demonstrate the solver's performance, accuracy, and scalability.

Main Methods:

  • An optimal control formulation is used, inverting for a stationary velocity field.
  • The solver employs a globalized, preconditioned, inexact reduced space Gauss–Newton–Krylov scheme.
  • State-of-the-art scientific computing techniques are leveraged for distributed-memory parallelism.

Main Results:

  • CLAIRE scales effectively to thousands of distributed memory nodes.
  • Registration accuracy is demonstrated on synthetic and real neuroimaging datasets.
  • A speedup of 5x on average (up to 17x peak) is achieved compared to previous work.

Conclusions:

  • CLAIRE provides an effective and scalable solution for 3D diffeomorphic image registration.
  • The solver achieves clinically relevant registration times (2-4 minutes per node) with excellent data fidelity.
  • An improved preconditioner enhances convergence speed, making CLAIRE suitable for high-performance computing platforms.