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Bayesian optimization of perfusion and transit time estimation in PASL-MRI.

Nuno Santos1, João Sanches, Patrícia Figueiredo

  • 1Siemens SA, Healthcare Sector and with Instituto Superior Técnico, Lisboa, Portugal. njgsantos@gmail.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Summary

This study introduces a Bayesian estimation method for Pulsed Arterial Spin Labeling (PASL) to improve brain perfusion and arterial transit time quantification. Combining this method with optimal sampling points minimizes estimation errors for more accurate non-invasive measurements.

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Biomedical Engineering

Background:

  • Pulsed Arterial Spin Labeling (PASL) offers non-invasive brain perfusion and arterial transit time quantification.
  • Low signal-to-noise ratio (SNR) and model parameter uncertainty in PASL data challenge accurate estimation.
  • Kinetic modeling requires fitting data across multiple inversion time points (TI).

Purpose of the Study:

  • To develop and evaluate a Bayesian estimation method for improved PASL parameter quantification.
  • To compare the proposed method against conventional Least Squares (LS) approaches.
  • To optimize the selection of inversion time (TI) sampling points for enhanced accuracy.

Main Methods:

  • Employed a two-compartment kinetic model with a Maximum a Posteriori (MAP) criterion.
  • Utilized a priori physiological information to guide model parameter estimation.
  • Conducted Monte Carlo simulations to compare Bayesian estimation with LS, using uniform and optimal TI sampling strategies.

Main Results:

  • The Bayesian estimation method significantly minimized estimation errors compared to LS.
  • Optimal TI sampling strategies, guided by the MAP criterion, further improved accuracy.
  • The combination of Bayesian estimation and optimal sampling yielded the most accurate results.

Conclusions:

  • PASL-based brain perfusion and arterial transit time measurements benefit from a Bayesian approach.
  • This approach optimizes both the sampling strategy and the estimation algorithm.
  • Incorporating prior physiological information enhances the reliability of PASL quantification.