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Parallel data resampling and Fourier inversion by the scan-line method.

D C Noll1, J A Webb, T E Warfel

  • 1Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA.

IEEE Transactions on Medical Imaging
|January 1, 1995
PubMed
Summary
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This study introduces a scan-line method for Fourier inversion, enhancing image reconstruction efficiency in medical imaging. This parallel computing approach improves magnetic resonance imaging data processing.

Area of Science:

  • Medical Imaging
  • Parallel Computing
  • Signal Processing

Background:

  • Fourier inversion is crucial for image reconstruction in computed tomography and magnetic resonance imaging.
  • Conventional Fourier inversion involves data interpolation and inverse Fourier transformation, often requiring significant computational resources.

Purpose of the Study:

  • To present an efficient scan-line method for Fourier inversion suitable for parallel computing.
  • To describe the conditions for algorithmic equivalence and methods for accuracy improvement in scan-line implementations.

Main Methods:

  • Implementing interpolation and inverse Fourier transformation using only row operations and data transposes.
  • Developing the scan-line algorithm for parallel processing on the iWarp computer using the Adapt language.

Related Experiment Videos

  • Applying the algorithm to magnetic resonance data acquired via radial and spiral trajectories.

    • Main Results:

    • The scan-line method allows Fourier inversion to be implemented on parallel computers supporting row operations and data transposes.
    • Algorithmic equivalence to serial implementations is achieved under specific conditions, with methods provided for accuracy enhancement.
    • Successful application to magnetic resonance imaging data demonstrates the practical utility of the scan-line approach.

    Conclusions:

    • The scan-line method offers an efficient and parallelizable approach to Fourier inversion for image reconstruction.
    • This technique is particularly beneficial for processing complex magnetic resonance imaging data.
    • The study provides a foundation for further optimization of parallel Fourier inversion algorithms.