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Related Concept Videos

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Social comparison plays a fundamental role in the evaluation of personal success and self-worth. Rather than assessing our achievements in isolation, we interpret their significance relative to personal goals and critically in comparison to the performance of others. A grade of B in a mathematics exam might elicit pride if one's expectation was a C, yet result in disappointment if an A was anticipated or if peers achieved superior results. These comparative evaluations illustrate how both...
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Diffusion01:12

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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Comparison of source localization techniques in diffuse optical tomography for fNIRS application using a realistic

Julie Tremblay1,2, Eduardo Martínez-Montes3,4, Phetsamone Vannasing1

  • 1LIONLAB, Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Canada.

Biomedical Optics Express
|January 9, 2019
PubMed
Summary

Bayesian model averaging enhances functional near-infrared spectroscopy (fNIRS) tomography by improving accuracy and reducing errors, outperforming other methods for brain imaging. This technique shows promise for clinical applications.

Keywords:
(100.3190) Inverse problems(100.6890) Three-dimensional image processing(170.1470) Blood or tissue constituent monitoring(170.1610) Clinical applications(170.3010) Image reconstruction techniques(170.3660) Light propagation in tissues

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Medical Physics

Background:

  • Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technique gaining traction in research and clinical settings.
  • Accurate source localization of hemoglobin variations is crucial for fNIRS tomography, but reconstructing these images is an ill-posed problem.
  • Developing robust mathematical frameworks is essential for improving the performance of fNIRS imaging.

Purpose of the Study:

  • To compare the performance and limitations of various source localization techniques for fNIRS tomography.
  • To evaluate these methods using individual anatomical magnetic resonance imaging (MRI) for light propagation modeling.
  • To assess techniques including Tikhonov regularization, truncated singular value decomposition, back-projection, L1-norm regularization, minimum norm estimates, low resolution electromagnetic tomography, and Bayesian model averaging.

Main Methods:

  • Forward problem solved using Monte Carlo simulation of light propagation.
  • Inverse problem linearized using the Rytov approximation.
  • Comparison of source localization techniques via receiver operating characteristic analysis, blurring, and localization error measures, using simulations (n=450) and human participant data.

Main Results:

  • Bayesian model averaging demonstrated superior performance, improving specificity, accuracy, and reducing blurring and localization error, even with noise and deep sources.
  • Regularized least squares methods offered higher sensitivity but increased blurring.
  • L1-based methods yielded sparse solutions with low blurring and high specificity, but reduced sensitivity.

Conclusions:

  • Bayesian model averaging is a promising technique for diffuse optical tomography (DOT) and fNIRS, offering significant improvements in image reconstruction.
  • Different source localization methods present trade-offs between sensitivity, specificity, and spatial resolution.
  • Experimental validation using a visual fNIRS task confirmed the potential of these advanced reconstruction methods.