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GPU accelerated FDTD solver and its application in MRI.

J Chi1, F Liu, J Jin

  • 1School of Automation Engineering, Qingdao University, China. chijieru@yahoo.com.cn

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary
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A new graphics processing unit (GPU)-based parallel finite difference time domain (FDTD) solver accelerates computational electromagnetics (CEM) simulations for magnetic resonance imaging (MRI). This enables improved B1 shimming for better transmit B1 profiles.

Area of Science:

  • Computational Electromagnetics (CEM)
  • Medical Imaging Physics

Background:

  • The finite difference time domain (FDTD) method is crucial for computational electromagnetics but demands significant computational resources.
  • High-field magnetic resonance imaging (MRI) requires advanced techniques like B1 shimming for optimal performance, often involving complex simulations.

Purpose of the Study:

  • To develop and apply a graphics processing unit (GPU)-based parallel FDTD solver to address computational limitations.
  • To investigate a novel B1 shimming scheme for high-field MRI using the accelerated FDTD method.

Main Methods:

  • Implementation of a parallel FDTD solver utilizing GPU acceleration.
  • Application of the solver to simulate and optimize a new B1 shimming strategy for MRI.

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Main Results:

  • The GPU-based FDTD solver significantly reduced simulation runtime compared to traditional CPU implementations.
  • The novel B1 shimming scheme demonstrated considerably improved transmit B1 profiles in simulations.

Conclusions:

  • GPU acceleration makes large-scale CEM problems, such as those in advanced MRI, computationally feasible.
  • The developed FDTD solver and optimized shimming scheme advance the capabilities for high-field MRI research and development.