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Accelerating B-spline interpolation on GPUs: Application to medical image registration.

Orestis Zachariadis1, Andrea Teatini2, Nitin Satpute1

  • 1Department of Electronics and Computer Engineering, Universidad de Cordoba, Córdoba, Spain.

Computer Methods and Programs in Biomedicine
|April 14, 2020
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Summary

This study introduces a novel GPU implementation of B-spline interpolation (BSI) for faster medical image registration. The optimized BSI significantly accelerates non-rigid image registration, improving performance and accuracy for Image Guided Surgery applications.

Keywords:
B-splinesGPUMedical image processingMedical image registrationParallel computing

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Area of Science:

  • Medical Imaging
  • Computer-Aided Surgery
  • Scientific Computing

Background:

  • B-spline interpolation (BSI) is crucial for 3D modeling in medical imaging and Image Guided Surgery (IGS).
  • Image registration, essential for IGS, is computationally intensive, hindering real-time applications.
  • Current CPU limitations necessitate GPU acceleration for demanding IGS tasks.

Purpose of the Study:

  • To develop a novel GPU implementation of BSI to accelerate non-rigid image registration algorithms.
  • To optimize BSI for efficient computation in 3D medical imaging applications.
  • To enhance the performance and accuracy of Image Guided Surgery workflows.

Main Methods:

  • Implemented BSI on GPUs, minimizing data transfer and maximizing on-chip register file usage.
  • Reformulated BSI using trilinear interpolations to reduce computational complexity and enhance accuracy.
  • Integrated the optimized BSI into a liver deformation registration workflow for pre-clinical validation.

Main Results:

  • Achieved an average performance improvement of 6.5x and a 2x increase in interpolation accuracy compared to state-of-the-art GPU implementations.
  • Demonstrated up to a 34% acceleration of a non-rigid image registration algorithm (Free Form Deformation) through pre-clinical validation.
  • Validated the effectiveness of the GPU-accelerated BSI in a practical medical imaging registration scenario.

Conclusions:

  • The novel parallelization scheme effectively utilizes GPU resources, yielding significant performance and accuracy gains.
  • The optimized BSI implementation demonstrably enhances the performance of current medical imaging registration applications.
  • This GPU-accelerated BSI is a valuable advancement for real-time Image Guided Surgery and other medical imaging tasks.