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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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A Protocol for Real-time 3D Single Particle Tracking
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Stochastic optimization of three-dimensional non-Cartesian sampling trajectory.

Guanhua Wang1, Jon-Fredrik Nielsen1,2, Jeffrey A Fessler1,2

  • 1Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States.

Magnetic Resonance in Medicine
|April 17, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces SNOPY, a data-driven framework for optimizing 3D non-Cartesian MRI sampling trajectories. SNOPY improves image quality and reduces nerve stimulation by tailoring trajectories for specific hardware and contrast needs.

Keywords:
MRIdata-driven optimizationdeep learningimage acquisitionnon-Cartesian sampling

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Computational Imaging
  • Biomedical Engineering

Background:

  • Optimizing 3D k-space sampling trajectories is crucial for efficient MRI acquisition.
  • Designing these trajectories computationally is a significant challenge.

Purpose of the Study:

  • To develop a generalized framework for optimizing 3D non-Cartesian MRI sampling patterns using data-driven optimization.
  • To enable simultaneous optimization of image quality, hardware constraints, and reduced peripheral nerve stimulation (PNS).

Main Methods:

  • A differentiable simulation model was developed for gradient-based trajectory optimization.
  • The framework optimizes gradient waveforms or properties of existing trajectories (e.g., rotation angles).
  • Synergistic optimization with model-based or learning-based reconstruction methods was explored.

Main Results:

  • The Stochastic optimization framework for 3D NOn-Cartesian samPling trajectorY (SNOPY) improved image quality (NRMSE from 0.29 to 0.22 for a 3D kooshball trajectory).
  • SNOPY reduced NRMSE in prospective studies (1.19 to 0.97 for stack-of-stars trajectories).
  • Optimized gradient waveforms reduced perceived PNS from 'strong' to 'mild' for rotational EPI.

Conclusions:

  • SNOPY offers an efficient, data-driven, and optimization-based method for tailoring non-Cartesian MRI sampling trajectories.
  • This approach enhances MRI efficiency and patient comfort.