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Related Experiment Videos

Resampling of data between arbitrary grids using convolution interpolation.

V Rasche1, R Proksa, R Sinkus

  • 1Philips Research Laboratories, Division Technical Systems, Hamburg, Germany. V.Rasche@pfh.research.philips.com

IEEE Transactions on Medical Imaging
|July 23, 1999
PubMed
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This study introduces a fast convolution interpolation method for resampling data between arbitrary grids. It efficiently handles complex sampling patterns, crucial for medical imaging like MRI and CT.

Area of Science:

  • Medical Imaging
  • Computational Science
  • Data Science

Background:

  • Resampling data is essential for medical imaging applications like Magnetic Resonance Tomography (MRT) and Computer Tomography (CT).
  • Current gridding methods often require data resampling onto rectilinear grids, limiting flexibility with nonrectilinear sampling patterns.

Purpose of the Study:

  • To introduce a generalized convolution interpolation technique for resampling data between arbitrary grids.
  • To present a novel method for determining the sampling density function, essential for accurate resampling.

Main Methods:

  • The algorithm resamples data in two steps: arbitrary grid to rectilinear, then rectilinear to arbitrary grid.
  • A Voronoi diagram-based approach is used to rapidly and independently determine the sampling density function from arbitrary sample distributions.

Related Experiment Videos

Main Results:

  • The proposed technique enables fast and accurate data resampling between any two arbitrary grids.
  • The Voronoi diagram method effectively calculates the sampling density function for diverse and complex sampling patterns.

Conclusions:

  • The developed convolution interpolation method offers a versatile solution for data resampling in medical imaging and other fields.
  • This approach enhances the flexibility and efficiency of image reconstruction and data analysis involving non-standard sampling grids.