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Investigating the uncertainty of cellular microenvironment parameter estimations via diffusion MRI cytometry.

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|July 8, 2026
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Summary

This study identifies robust cell microenvironment parameters using IMPULSED diffusion MRI (dMRI) and develops a mapping framework for accurate estimation. This advances noninvasive monitoring of radiation therapy response.

Keywords:
MRI cytometrydiffusion MRIuncertainty

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Area of Science:

  • Biomedical Imaging
  • Quantitative MRI
  • Tumor Microenvironment Analysis

Background:

  • Cell microenvironment features are key biomarkers for assessing early tumor response to radiation therapy.
  • Diffusion MRI (dMRI) offers a noninvasive method to study these features, but conventional models have high uncertainty and poor robustness.

Purpose of the Study:

  • Establish a theoretical basis for robustly estimating cell microenvironment parameters from IMPULSED dMRI signals.
  • Develop a reliable mapping-based framework for accurate parameter estimation.

Main Methods:

  • Simulated dMRI signals using the IMPULSED model with PGSE and OGSE sequences.
  • Quantified parameter uncertainty via Jacobian-based sensitivity analysis.
  • Developed mapping models (linear regression, polynomial regression, neural network) using dimension-reduced, logarithmically transformed dMRI signals.

Main Results:

  • Identified cell diameter (d), intracellular volume fraction (Vin), and extracellular diffusion coefficient (Dex) as robustly derivable parameters with low uncertainty.
  • A 4-layer neural network achieved the best performance, with low mean absolute errors for d, Vin, and Dex.
  • In vitro validation showed a 6.7% error in cell diameter estimation.

Conclusions:

  • Successfully identified robust cell microenvironment parameters from IMPULSED dMRI.
  • Established a mapping-based framework for accurate and robust parameter estimation.
  • Provides a foundation for noninvasive monitoring of tumor microenvironment changes during radiation therapy.