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Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI.

Nicholas Dwork1, Corey A Baron2, Ethan M I Johnson3

  • 1Radiology and Biomedical Imaging, University of California in San Francisco, San Francisco, California, 94158, USA.

Magnetic Resonance Imaging
|November 24, 2020
PubMed
Summary

This study introduces an efficient algorithm for generating variable density poisson-disc sampling patterns. The method optimizes point placement, reducing computations and proving useful for compressed sensing applications.

Keywords:
Compressed sensingParallel imagingPoisson discSampling

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Area of Science:

  • Computer Science
  • Computational Geometry
  • Data Science

Background:

  • Poisson-disc sampling is crucial for generating uniformly distributed random points.
  • Existing methods can be computationally intensive, especially for variable density distributions.
  • Optimizing sampling patterns is key for efficient data acquisition and processing.

Purpose of the Study:

  • To develop a fast algorithm for generating variable density poisson-disc distributions.
  • To demonstrate the algorithm's flexibility in creating directional density variations.
  • To validate the applicability of these patterns in compressed sensing.

Main Methods:

  • Utilized a background grid array based on a minimum parameter value to track potential point conflicts.
  • Implemented a parameterized function to control and vary sampling density.
  • Integrated the algorithm with compressed sensing frameworks.

Main Results:

  • Achieved a significant reduction in computational cost through optimized conflict checking.
  • Successfully generated variable density poisson-disc patterns, including direction-dependent variations.
  • Demonstrated the suitability of the generated patterns for compressed sensing.

Conclusions:

  • The proposed method offers an efficient approach to generating complex poisson-disc distributions.
  • The algorithm's adaptability makes it valuable for various applications, notably compressed sensing.
  • Further enhancements include generating patterns with controlled acceleration rates.