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Fast GPU 3D diffeomorphic image registration.

Malte Brunn1, Naveen Himthani2, George Biros2

  • 1University of Stuttgart, Universitätsstraße 38, Stuttgart 70569 Germany.

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|December 31, 2020
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Summary
This summary is machine-generated.

This study introduces a faster GPU-accelerated solver for 3D medical image registration, significantly reducing computation time for large deformation diffeomorphic registration. The new algorithms achieve substantial speed-ups, enabling rapid analysis of complex medical imaging data.

Keywords:
Diffeomorphic Image RegistrationGPU computingGauss–Newton–Krylov MethodMixed-Precision SolverParallel Optimization

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Area of Science:

  • Medical Image Analysis
  • Computational Imaging
  • Scientific Computing

Background:

  • 3D image registration is crucial but computationally intensive in medical image analysis.
  • Existing large deformation diffeomorphic registration packages have limited GPU support.
  • The CLAIRE library is a key tool, but its performance on GPUs needs enhancement.

Purpose of the Study:

  • To develop and implement a mixed-precision, Gauss-Newton-Krylov solver for accelerated 3D diffeomorphic image registration.
  • To extend the CLAIRE library with GPU acceleration for improved computational efficiency.
  • To significantly reduce the runtime of derivative calculation and scattered-data interpolation kernels.

Main Methods:

  • Implemented a mixed-precision, Gauss-Newton-Krylov solver on GPU architectures.
  • Developed highly-optimized, mixed-precision GPU kernels for scattered-data interpolation.
  • Replaced Fast-Fourier-Transform (FFT)-based derivatives with optimized 8th-order finite differences.
  • Extended the CLAIRE library for GPU computation.

Main Results:

  • Achieved registration of 256^3 clinical images in under 6 seconds on a single NVIDIA Tesla V100 GPU.
  • Demonstrated over 20x speed-up compared to the CPU version of CLAIRE.
  • Showcased over 30x speed-up compared to existing GPU implementations.
  • Significantly reduced computational time for key registration kernels.

Conclusions:

  • The developed GPU-accelerated solver dramatically enhances the speed of 3D diffeomorphic image registration.
  • This advancement makes large-scale medical image analysis more feasible and efficient.
  • The extended CLAIRE library offers a powerful tool for researchers needing high-performance image registration.