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A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.

Huixuan Wu1, Pan Du2, Rohan Kokate1

  • 1Department of Aerospace Engineering, School of Engineering, University of Kansas, Lawrence, Kansas, United States of America.

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|July 9, 2021
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This study introduces a new semi-analytical solution for magnetic particle tracking, improving trajectory reconstruction accuracy. Deep neural networks (DNNs) and hybrid methods enhance denoising for precise particle movement analysis.

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Area of Science:

  • Physics
  • Engineering
  • Data Science

Background:

  • Magnetic particle tracking (MPT) measures particle translation and rotation in opaque flows.
  • Current trajectory reconstruction methods often rely on numerical optimization or filtering with artificial parameters.
  • Existing analytical algorithms have limitations, particularly regarding magnetic field gradient measurement.

Purpose of the Study:

  • To present a novel semi-analytical solution and reconstruction algorithm for MPT.
  • To develop deep neural network (DNN)-based models for denoising MPT trajectories.
  • To compare the performance of DNNs, wavelet filtering, and hybrid methods for trajectory reconstruction.

Main Methods:

  • A new semi-analytical solution for MPT trajectory reconstruction is proposed.
  • Deep neural network (DNN) models are developed for trajectory denoising.
  • Hybrid methods combining wavelet filtering and DNNs are investigated for velocity reconstruction.

Main Results:

  • The new semi-analytical method supports arbitrary sensor arrangements.
  • DNN-based denoisers show superior accuracy in position reconstruction compared to traditional methods.
  • Hybrid wavelet-DNN methods provide more accurate velocity reconstruction, while all methods perform well in orientation reconstruction.

Conclusions:

  • The developed semi-analytical solution offers a versatile approach to MPT trajectory reconstruction.
  • DNN-based denoising significantly improves position accuracy in MPT.
  • Hybrid methods effectively address velocity signal over-smoothing issues, enhancing overall MPT data quality.